A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Learning is posed as a problem of function estimation, for which two principles of solution are considered: empirical risk minimization and structural risk minimization. These two principles are applied to two different statements of the function estimation problem: global and local. Systematic improvements in prediction power are illustrated in application to zip-code recognition. <s> BIB001 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> The problem of learning a semantic representation of a text document from data is addressed, in the situation where a corpus of unlabeled paired documents is available, each pair being formed by a short English document and its French translation. This representation can then be used for any retrieval, categorization or clustering task, both in a standard and in a cross-lingual setting. By using kernel functions, in this case simple bag-of-words inner products, each part of the corpus is mapped to a high-dimensional space. The correlations between the two spaces are then learnt by using kernel Canonical Correlation Analysis. A set of directions is found in the first and in the second space that are maximally correlated. Since we assume the two representations are completely independent apart from the semantic content, any correlation between them should reflect some semantic similarity. Certain patterns of English words that relate to a specific meaning should correlate with certain patterns of French words corresponding to the same meaning, across the corpus. Using the semantic representation obtained in this way we first demonstrate that the correlations detected between the two versions of the corpus are significantly higher than random, and hence that a representation based on such features does capture statistical patterns that should reflect semantic information. Then we use such representation both in cross-language and in single-language retrieval tasks, observing performance that is consistently and significantly superior to LSI on the same data. <s> BIB002 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so. <s> BIB003 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semi-supervised learning. Although a number of such methods are proposed, at the current stage, we still don't have a complete understanding of their effectiveness. This paper investigates a closely related problem, which leads to a novel approach to semi-supervised learning. Specifically we consider learning predictive structures on hypothesis spaces (that is, what kind of classifiers have good predictive power) from multiple learning tasks. We present a general framework in which the structural learning problem can be formulated and analyzed theoretically, and relate it to learning with unlabeled data. Under this framework, algorithms for structural learning will be proposed, and computational issues will be investigated. Experiments will be given to demonstrate the effectiveness of the proposed algorithms in the semi-supervised learning setting. <s> BIB004 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We present a nonparametric method which directly produces resampling weights without distribution estimation. Our method works by matching distributions between training and testing sets in feature space. Experimental results demonstrate that our method works well in practice. <s> BIB005 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> MOTIVATION ::: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based statistical test for this problem, based on the fact that two distributions are different if and only if there exists at least one function having different expectation on the two distributions. Consequently we use the maximum discrepancy between function means as the basis of a test statistic. The Maximum Mean Discrepancy (MMD) can take advantage of the kernel trick, which allows us to apply it not only to vectors, but strings, sequences, graphs, and other common structured data types arising in molecular biology. ::: ::: ::: RESULTS ::: We study the practical feasibility of an MMD-based test on three central data integration tasks: Testing cross-platform comparability of microarray data, cancer diagnosis, and data-content based schema matching for two different protein function classification schemas. In all of these experiments, including high-dimensional ones, MMD is very accurate in finding samples that were generated from the same distribution, and outperforms its best competitors. ::: ::: ::: CONCLUSIONS ::: We have defined a novel statistical test of whether two samples are from the same distribution, compatible with both multivariate and structured data, that is fast, easy to implement, and works well, as confirmed by our experiments. ::: ::: ::: AVAILABILITY ::: http://www.dbs.ifi.lmu.de/~borgward/MMD. <s> BIB006 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> We describe an approach to domain adaptation that is appropriate exactly in the case when one has enough ``target'' data to do slightly better than just using only ``source'' data. Our approach is incredibly simple, easy to implement as a preprocessing step (10 lines of Perl!) and outperforms state-of-the-art approaches on a range of datasets. Moreover, it is trivially extended to a multi-domain adaptation problem, where one has data from a variety of different domains. <s> BIB007 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Many multimedia applications can benefit from techniques for adapting existing classifiers to data with different distributions. One example is cross-domain video concept detection which aims to adapt concept classifiers across various video domains. In this paper, we explore two key problems for classifier adaptation: (1) how to transform existing classifier(s) into an effective classifier for a new dataset that only has a limited number of labeled examples, and (2) how to select the best existing classifier(s) for adaptation. For the first problem, we propose Adaptive Support Vector Machines (A-SVMs) as a general method to adapt one or more existing classifiers of any type to the new dataset. It aims to learn the "delta function" between the original and adapted classifier using an objective function similar to SVMs. For the second problem, we estimate the performance of each existing classifier on the sparsely-labeled new dataset by analyzing its score distribution and other meta features, and select the classifiers with the best estimated performance. The proposed method outperforms several baseline and competing methods in terms of classification accuracy and efficiency in cross-domain concept detection in the TRECVID corpus. <s> BIB008 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the distance function. We express this problem as a particular Bregman optimization problem---that of minimizing the LogDet divergence subject to linear constraints. Our resulting algorithm has several advantages over existing methods. First, our method can handle a wide variety of constraints and can optionally incorporate a prior on the distance function. Second, it is fast and scalable. Unlike most existing methods, no eigenvalue computations or semi-definite programming are required. We also present an online version and derive regret bounds for the resulting algorithm. Finally, we evaluate our method on a recent error reporting system for software called Clarify, in the context of metric learning for nearest neighbor classification, as well as on standard data sets. <s> BIB009 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the training and testing problems have different distributions or features. In this paper, we consider transfer learning via dimensionality reduction. To solve this problem, we learn a low-dimensional latent feature space where the distributions between the source domain data and the target domain data are the same or close to each other. Onto this latent feature space, we project the data in related domains where we can apply standard learning algorithms to train classification or regression models. Thus, the latent feature space can be treated as a bridge of transferring knowledge from the source domain to the target domain. The main contribution of our work is that we propose a new dimensionality reduction method to find a latent space, which minimizes the distance between distributions of the data in different domains in a latent space. The effectiveness of our approach to transfer learning is verified by experiments in two real world applications: indoor WiFi localization and binary text classification. <s> BIB010 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Exploding amounts of multimedia data increasingly require automatic indexing and classification, e.g. training classifiers to produce high-level features, or semantic concepts, chosen to represent image content, like car, person, etc. When changing the applied domain (i.e. from news domain to consumer home videos), the classifiers trained in one domain often perform poorly in the other domain due to changes in feature distributions. Additionally, classifiers trained on the new domain alone may suffer from too few positive training samples. Appropriately adapting data/models from an old domain to help classify data in a new domain is an important issue. In this work, we develop a new cross-domain SVM (CDSVM) algorithm for adapting previously learned support vectors from one domain to help classification in another domain. Better precision is obtained with almost no additional computational cost. Also, we give a comprehensive summary and comparative study of the state- of-the-art SVM-based cross-domain learning methods. Evaluation over the latest large-scale TRECVID benchmark data set shows that our CDSVM method can improve mean average precision over 36 concepts by 7.5%. For further performance gain, we also propose an intuitive selection criterion to determine which cross-domain learning method to use for each concept. <s> BIB011 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. In many real world applications, however, we wish to make use of the labeled data from one domain (called in-domain) to classify the unlabeled data in a different domain (out-of-domain). This problem often happens when obtaining labeled data in one domain is difficult while there are plenty of labeled data from a related but different domain. In general, this is a transfer learning problem where we wish to classify the unlabeled data through the labeled data even though these data are not from the same domain. In this paper, we formulate this domain-transfer learning problem under a novel spectral classification framework, where the objective function is introduced to seek consistency between the in-domain supervision and the out-of-domain intrinsic structure. Through optimization of the cost function, the label information from the in-domain data is effectively transferred to help classify the unlabeled data from the out-of-domain. We conduct extensive experiments to evaluate our method and show that our algorithm achieves significant improvements on classification performance over many state-of-the-art algorithms. <s> BIB012 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Our objective is to obtain a state-of-the art object category detector by employing a state-of-the-art image classifier to search for the object in all possible image sub-windows. We use multiple kernel learning of Varma and Ray (ICCV 2007) to learn an optimal combination of exponential χ2 kernels, each of which captures a different feature channel. Our features include the distribution of edges, dense and sparse visual words, and feature descriptors at different levels of spatial organization. <s> BIB013 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Recently there has been increasing interest in the problem of transfer learning, in which the typical assumption that training and testing data are drawn from identical distributions is relaxed. We specifically address the problem of transductive transfer learning in which we have access to labeled training data and unlabeled testing data potentially drawn from different, yet related distributions, and the goal is to leverage the labeled training data to learn a classifier to correctly predict data from the testing distribution. We have derived efficient algorithms for transductive transfer learning based on a novel viewpoint and the Support Vector Machine (SVM) paradigm, of a large margin hyperplane classifier in a feature space. We show that our method can out-perform some recent state-of-the-art approaches for transfer learning on several data sets, with the added benefits of model and data separation and the potential to leverage existing work on support vector machines. <s> BIB014 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> The regularization principals [31] lead approximation schemes to deal with various learning problems, e.g., the regularization of the norm in a reproducing kernel Hilbert space for the ill-posed problem. In this paper, we present a family of subspace learning algorithms based on a new form of regularization, which transfers the knowledge gained in training samples to testing samples. In particular, the new regularization minimizes the Bregman divergence between the distribution of training samples and that of testing samples in the selected subspace, so it boosts the performance when training and testing samples are not independent and identically distributed. To test the effectiveness of the proposed regularization, we introduce it to popular subspace learning algorithms, e.g., principal components analysis (PCA) for cross-domain face modeling; and Fisher's linear discriminant analysis (FLDA), locality preserving projections (LPP), marginal Fisher's analysis (MFA), and discriminative locality alignment (DLA) for cross-domain face recognition and text categorization. Finally, we present experimental evidence on both face image data sets and text data sets, suggesting that the proposed Bregman divergence-based regularization is effective to deal with cross-domain learning problems. <s> BIB015 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of users publishing sentiment data (e.g., reviews, blogs). Although traditional classification algorithms can be used to train sentiment classifiers from manually labeled text data, the labeling work can be time-consuming and expensive. Meanwhile, users often use some different words when they express sentiment in different domains. If we directly apply a classifier trained in one domain to other domains, the performance will be very low due to the differences between these domains. In this work, we develop a general solution to sentiment classification when we do not have any labels in a target domain but have some labeled data in a different domain, regarded as source domain. In this cross-domain sentiment classification setting, to bridge the gap between the domains, we propose a spectral feature alignment (SFA) algorithm to align domain-specific words from different domains into unified clusters, with the help of domain-independent words as a bridge. In this way, the clusters can be used to reduce the gap between domain-specific words of the two domains, which can be used to train sentiment classifiers in the target domain accurately. Compared to previous approaches, SFA can discover a robust representation for cross-domain data by fully exploiting the relationship between the domain-specific and domain-independent words via simultaneously co-clustering them in a common latent space. We perform extensive experiments on two real world datasets, and demonstrate that SFA significantly outperforms previous approaches to cross-domain sentiment classification. <s> BIB016 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> We propose a novel problem formulation of learning a single task when the data are provided in different feature spaces. Each such space is called an outlook, and is assumed to contain both labeled and unlabeled data. The objective is to take advantage of the data from all the outlooks to better classify each of the outlooks. We devise an algorithm that computes optimal affine mappings from different outlooks to a target outlook by matching moments of the empirical distributions. We further derive a probabilistic interpretation of the resulting algorithm and a sample complexity bound indicating how many samples are needed to adequately find the mapping. We report the results of extensive experiments on activity recognition tasks that show the value of the proposed approach in boosting performance. <s> BIB017 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Labeled examples are often expensive and time-consuming to obtain. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have (a) different feature spaces (e.g., image vs. text data), (b) different data distributions, and (c) different output spaces? This paper proposes a solution and discusses the conditions where this is possible and highly likely to produce better results. It works by first using spectral embedding to unify the different feature spaces of the target and source data sets, even when they have completely different feature spaces. The principle is to cast into an optimization objective that preserves the original structure of the data, while at the same time, maximizes the similarity between the two. Second, a judicious sample selection strategy is applied to select only those related source examples. At last, a Bayesian-based approach is applied to model the relationship between different output spaces. The three steps can bridge related heterogeneous sources in order to learn the target task. Among the 12 experiment data sets, for example, the images with wavelet-transformed-based features are used to predict another set of images whose features are constructed from color-histogram space. By using these extracted examples from heterogeneous sources, the models can reduce the error rate by as much as~50\%, compared with the methods using only the examples from the target task. <s> BIB018 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> We first propose a new spatio-temporal context distribution feature of interest points for human action recognition. Each action video is expressed as a set of relative XYT coordinates between pairwise interest points in a local region. We learn a global GMM (referred to as Universal Background Model, UBM) using the relative coordinate features from all the training videos, and then represent each video as the normalized parameters of a video-specific GMM adapted from the global GMM. In order to capture the spatio-temporal relationships at different levels, multiple GMMs are utilized to describe the context distributions of interest points over multi-scale local regions. To describe the appearance information of an action video, we also propose to use GMM to characterize the distribution of local appearance features from the cuboids centered around the interest points. Accordingly, an action video can be represented by two types of distribution features: 1) multiple GMM distributions of spatio-temporal context; 2) GMM distribution of local video appearance. To effectively fuse these two types of heterogeneous and complementary distribution features, we additionally propose a new learning algorithm, called Multiple Kernel Learning with Augmented Features (AFMKL), to learn an adapted classifier based on multiple kernels and the pre-learned classifiers of other action classes. Extensive experiments on KTH, multi-view IXMAS and complex UCF sports datasets demonstrate that our method generally achieves higher recognition accuracy than other state-of-the-art methods. <s> BIB019 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Domain adaptation allows knowledge from a source domain to be transferred to a different but related target domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we first propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a reproducing kernel Hilbert space using maximum mean miscrepancy. In the subspace spanned by these transfer components, data properties are preserved and data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. Furthermore, in order to uncover the knowledge hidden in the relations between the data labels from the source and target domains, we extend TCA in a semisupervised learning setting, which encodes label information into transfer components learning. We call this extension semisupervised TCA. The main contribution of our work is that we propose a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation. We propose both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce the distance between domain distributions by projecting data onto the learned transfer components. Finally, our approach can handle large datasets and naturally lead to out-of-sample generalization. The effectiveness and efficiency of our approach are verified by experiments on five toy datasets and two real-world applications: cross-domain indoor WiFi localization and cross-domain text classification. <s> BIB020 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features can change from one dataset to the next. In this paper, we address the problem of visual domain adaptation for transferring object models from one dataset or visual domain to another. We introduce ARC-t, a flexible model for supervised learning of non-linear transformations between domains. Our method is based on a novel theoretical result demonstrating that such transformations can be learned in kernel space. Unlike existing work, our model is not restricted to symmetric transformations, nor to features of the same type and dimensionality, making it applicable to a significantly wider set of adaptation scenarios than previous methods. Furthermore, the method can be applied to categories that were not available during training. We demonstrate the ability of our method to adapt object recognition models under a variety of situations, such as differing imaging conditions, feature types and codebooks. <s> BIB021 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications. <s> BIB022 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> The selection of software metrics for building software quality prediction models is a search-based software engineering problem. An exhaustive search for such metrics is usually not feasible due to limited project resources, especially if the number of available metrics is large. Defect prediction models are necessary in aiding project managers for better utilizing valuable project resources for software quality improvement. The efficacy and usefulness of a fault-proneness prediction model is only as good as the quality of the software measurement data. This study focuses on the problem of attribute selection in the context of software quality estimation. A comparative investigation is presented for evaluating our proposed hybrid attribute selection approach, in which feature ranking is first used to reduce the search space, followed by a feature subset selection. A total of seven different feature ranking techniques are evaluated, while four different feature subset selection approaches are considered. The models are trained using five commonly used classification algorithms. The case study is based on software metrics and defect data collected from multiple releases of a large real-world software system. The results demonstrate that while some feature ranking techniques performed similarly, the automatic hybrid search algorithm performed the best among the feature subset selection methods. Moreover, performances of the defect prediction models either improved or remained unchanged when over 85were eliminated. Copyright © 2011 John Wiley & Sons, Ltd. <s> BIB023 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep networks that learn features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classifier is trained with audio-only data but tested with video-only data and vice-versa. Our models are validated on the CUAVE and AVLetters datasets on audio-visual speech classification, demonstrating best published visual speech classification on AVLetters and effective shared representation learning. <s> BIB024 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. To cope with the considerable change between feature distributions of different domains, we propose a new cross-domain kernel learning framework into which many existing kernel methods can be readily incorporated. Our framework, referred to as Domain Transfer Multiple Kernel Learning (DTMKL), simultaneously learns a kernel function and a robust classifier by minimizing both the structural risk functional and the distribution mismatch between the labeled and unlabeled samples from the auxiliary and target domains. Under the DTMKL framework, we also propose two novel methods by using SVM and prelearned classifiers, respectively. Comprehensive experiments on three domain adaptation data sets (i.e., TRECVID, 20 Newsgroups, and email spam data sets) demonstrate that DTMKL-based methods outperform existing cross-domain learning and multiple kernel learning methods. <s> BIB025 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> In real-world applications of visual recognition, many factors — such as pose, illumination, or image quality — can cause a significant mismatch between the source domain on which classifiers are trained and the target domain to which those classifiers are applied. As such, the classifiers often perform poorly on the target domain. Domain adaptation techniques aim to correct the mismatch. Existing approaches have concentrated on learning feature representations that are invariant across domains, and they often do not directly exploit low-dimensional structures that are intrinsic to many vision datasets. In this paper, we propose a new kernel-based method that takes advantage of such structures. Our geodesic flow kernel models domain shift by integrating an infinite number of subspaces that characterize changes in geometric and statistical properties from the source to the target domain. Our approach is computationally advantageous, automatically inferring important algorithmic parameters without requiring extensive cross-validation or labeled data from either domain. We also introduce a metric that reliably measures the adaptability between a pair of source and target domains. For a given target domain and several source domains, the metric can be used to automatically select the optimal source domain to adapt and avoid less desirable ones. Empirical studies on standard datasets demonstrate the advantages of our approach over competing methods. <s> BIB026 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> In this paper, we propose a semi-supervised kernel matching method to address domain adaptation problems where the source distribution substantially differs from the target distribution. Specifically, we learn a prediction function on the labeled source data while mapping the target data points to similar source data points by matching the target kernel matrix to a submatrix of the source kernel matrix based on a Hilbert Schmidt Independence Criterion. We formulate this simultaneous learning and mapping process as a non-convex integer optimization problem and present a local minimization procedure for its relaxed continuous form. Our empirical results show the proposed kernel matching method significantly outperforms alternative methods on the task of across domain sentiment classification. <s> BIB027 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods. <s> BIB028 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. Recently, they have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains. SDAs learn robust data representations by reconstruction, recovering original features from data that are artificially corrupted with noise. In this paper, we propose marginalized SDA (mSDA) that addresses two crucial limitations of SDAs: high computational cost and lack of scalability to high-dimensional features. In contrast to SDAs, our approach of mSDA marginalizes noise and thus does not require stochastic gradient descent or other optimization algorithms to learn parameters--in fact, they are computed in closed-form. Consequently, mSDA, which can be implemented in only 20 lines of MATLAB™, significantly speeds up SDAs by two orders of magnitude. Furthermore, the representations learnt by mSDA are as effective as the traditional SDAs, attaining almost identical accuracies in benchmark tasks. <s> BIB029 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Transfer learning is established as an effective technology in computer vision for leveraging rich labeled data in the source domain to build an accurate classifier for the target domain. However, most prior methods have not simultaneously reduced the difference in both the marginal distribution and conditional distribution between domains. In this paper, we put forward a novel transfer learning approach, referred to as Joint Distribution Adaptation (JDA). Specifically, JDA aims to jointly adapt both the marginal distribution and conditional distribution in a principled dimensionality reduction procedure, and construct new feature representation that is effective and robust for substantial distribution difference. Extensive experiments verify that JDA can significantly outperform several state-of-the-art methods on four types of cross-domain image classification problems. <s> BIB030 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Machine learning classifiers have recently emerged as a way to predict the introduction of bugs in changes made to source code files. The classifier is first trained on software history, and then used to predict if an impending change causes a bug. Drawbacks of existing classifier-based bug prediction techniques are insufficient performance for practical use and slow prediction times due to a large number of machine learned features. This paper investigates multiple feature selection techniques that are generally applicable to classification-based bug prediction methods. The techniques discard less important features until optimal classification performance is reached. The total number of features used for training is substantially reduced, often to less than 10 percent of the original. The performance of Naive Bayes and Support Vector Machine (SVM) classifiers when using this technique is characterized on 11 software projects. Naive Bayes using feature selection provides significant improvement in buggy F-measure (21 percent improvement) over prior change classification bug prediction results (by the second and fourth authors [28]). The SVM's improvement in buggy F-measure is 9 percent. Interestingly, an analysis of performance for varying numbers of features shows that strong performance is achieved at even 1 percent of the original number of features. <s> BIB031 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Domain transfer learning, which learns a target classifier using labeled data from a different distribution, has shown promising value in knowledge discovery yet still been a challenging problem. Most previous works designed adaptive classifiers by exploring two learning strategies independently: distribution adaptation and label propagation. In this paper, we propose a novel transfer learning framework, referred to as Adaptation Regularization based Transfer Learning (ARTL), to model them in a unified way based on the structural risk minimization principle and the regularization theory. Specifically, ARTL learns the adaptive classifier by simultaneously optimizing the structural risk functional, the joint distribution matching between domains, and the manifold consistency underlying marginal distribution. Based on the framework, we propose two novel methods using Regularized Least Squares (RLS) and Support Vector Machines (SVMs), respectively, and use the Representer theorem in reproducing kernel Hilbert space to derive corresponding solutions. Comprehensive experiments verify that ARTL can significantly outperform state-of-the-art learning methods on several public text and image datasets. <s> BIB032 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> In this paper, we study the heterogeneous domain adaptation (HDA) problem, in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. By introducing two different projection matrices, we first transform the data from two domains into a common subspace such that the similarity between samples across different domains can be measured. We then propose a new feature mapping function for each domain, which augments the transformed samples with their original features and zeros. Existing supervised learning methods ( e.g., SVM and SVR) can be readily employed by incorporating our newly proposed augmented feature representations for supervised HDA. As a showcase, we propose a novel method called Heterogeneous Feature Augmentation (HFA) based on SVM. We show that the proposed formulation can be equivalently derived as a standard Multiple Kernel Learning (MKL) problem, which is convex and thus the global solution can be guaranteed. To additionally utilize the unlabeled data in the target domain, we further propose the semi-supervised HFA (SHFA) which can simultaneously learn the target classifier as well as infer the labels of unlabeled target samples. Comprehensive experiments on three different applications clearly demonstrate that our SHFA and HFA outperform the existing HDA methods. <s> BIB033 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> In this paper, we present an efficient multi-class heterogeneous domain adaptation method, where data from source and target domains are represented by heterogeneous features of different dimensions. Specifically, we propose to reconstruct a sparse feature transformation matrix to map the weight vector of classifiers learned from the source domain to the target domain. We cast this learning task as a compressed sensing problem, where each binary classifier induced from multiple classes can be deemed as a measurement sensor. Based on the compressive sensing theory, the estimation error of the transformation matrix decreases with the increasing number of classifiers. Therefore, to guarantee reconstruction performance, we construct sufficiently many binary classifiers based on the error correcting output coding. Extensive experiments are conducted on both a toy dataset and three real-world datasets to verify the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy. <s> BIB034 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Cross-project defect prediction (CPDP) has been deemed as an emerging technology of software quality assurance, especially in new or inactive projects, and a few improved methods have been proposed to support better defect prediction. However, the regular CPDP always assumes that the features of training and test data are all identical. Hence, very little is known about whether the method for CPDP with imbalanced feature sets (CPDP-IFS) works well. Considering the diversity of defect data sets available on the Internet as well as the high cost of labeling data, to address the issue, in this paper we proposed a simple approach according to a distribution characteristic-based instance (object class) mapping, and demonstrated the validity of our method based on three public defect data sets (i.e., PROMISE, ReLink and AEEEM). Besides, the empirical results indicate that the hybrid model composed of CPDP and CPDP-IFS does improve the prediction performance of the regular CPDP to some extent. <s> BIB035 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Most previous heterogeneous transfer learning methods learn a cross-domain feature mapping between heterogeneous feature spaces based on a few cross-domain instance-correspondences, and these corresponding instances are assumed to be representative in the source and target domains respectively. However, in many real-world scenarios, this assumption may not hold. As a result, the constructed feature mapping may not be precise due to the bias issue of the correspondences in the target or (and) source domain(s). In this case, a classifier trained on the labeled transformed-source-domain data may not be useful for the target domain. In this paper, we present a new transfer learning framework called Hybrid Heterogeneous Transfer Learning (HHTL), which allows the corresponding instances across domains to be biased in either the source or target domain. Specifically, we propose a deep learning approach to learn a feature mapping between cross-domain heterogeneous features as well as a better feature representation for mapped data to reduce the bias issue caused by the cross-domain correspondences. Extensive experiments on several multilingual sentiment classification tasks verify the effectiveness of our proposed approach compared with some baseline methods. <s> BIB036 </s> A survey of transfer learning <s> Asymmetric feature-based transfer learning <s> Software defect prediction is one of the most active research areas in software engineering. We can build a prediction model with defect data collected from a software project and predict defects in the same project, i.e. within-project defect prediction (WPDP). Researchers also proposed cross-project defect prediction (CPDP) to predict defects for new projects lacking in defect data by using prediction models built by other projects. In recent studies, CPDP is proved to be feasible. However, CPDP requires projects that have the same metric set, meaning the metric sets should be identical between projects. As a result, current techniques for CPDP are difficult to apply across projects with heterogeneous metric sets. To address the limitation, we propose heterogeneous defect prediction (HDP) to predict defects across projects with heterogeneous metric sets. Our HDP approach conducts metric selection and metric matching to build a prediction model between projects with heterogeneous metric sets. Our empirical study on 28 subjects shows that about 68% of predictions using our approach outperform or are comparable to WPDP with statistical significance. <s> BIB037
In an early and often cited work, Daumé BIB007 proposes a simple domain adaptation algorithm, referred to as the feature augmentation method (FAM), requiring only ten lines of Perl script that uses labeled source data and limited labeled target data. In a transfer learning environment, there are scenarios where a feature in the source domain may have a different meaning in the target domain. The issue is referred to as context feature bias, which causes the conditional distributions between the source and target domains to be different. To resolve context feature bias, a method to augment the source and target feature space with three duplicate copies of the original feature set is proposed. More specifically, the three duplicate copies of the original feature set in the augmented source feature space represent a common feature set, a source specific feature set, and a target specific feature set which is always set to zero. In a similar way, the three duplicate copies of the original feature set in the augmented target feature space represent a common feature set, a source specific feature set which is always set to zero, and a target specific feature set. By performing this feature augmentation, the feature space is duplicated three times. From the feature augmentation structure, a classifier learns the individual feature weights for the augmented feature set, which will help correct for any feature bias issues. Using a text document example where features are modeled as a bagof-words, a common word like "the" would be assigned (through the learning process) a high weight for the common feature set, and a word that is different between the source and target like "monitor" would be assigned a high weight for the corresponding domain feature set. The duplication of features creates feature separation between the source and target domains, and allows the final classifier to learn the optimal feature weights. For the experiments, a number of different natural language processing applications are tested and in each case the classification error rate is measured as the performance metric. An SVM learner is used to implement the Daumé BIB007 approach. A number of baseline approaches with no transfer learning techniques are measured along with a method by Chelba  . The test results show the Daumé BIB007 method is able to outperform the other methods tested. However, when the source and target domains are very similar, the Daumé BIB007 approach tends to underperform. The reason for the underperformance is the duplication of feature sets represents irrelevant and noisy information when the source and target domains are very similar. Multiple kernel learning is a technique used in traditional machine learning algorithms as demonstrated in the works of Wu BIB019 and Vedaldi BIB013 . Multiple kernel learning allows for an optimal kernel function to be learned in a computationally efficient manner. The paper by Duan BIB025 proposes to implement a multiple kernel learning framework for a transfer learning environment called the domain transfer multiple kernel learning (DTMKL). Instead of learning one kernel, multiple kernel learning assumes the kernel is comprised of a linear combination of multiple predefined base kernels. The final classifier and the kernel function are learned simultaneously which has the advantage of using labeled data during the kernel learning process. This is an improvement over Pan BIB010 and Huang BIB005 where a two-stage approach is used. The final classifier learning process minimizes the structural risk functional BIB001 and the marginal distribution between domains using the maximum mean discrepancy measure BIB006 . Pseudo labels are found for the unlabeled target data to take advantage of this information during the learning process. The pseudo labels are found as a weighted combination of base classifiers (one for each feature) trained from the labeled source data. A regularization term is added to the optimization problem to ensure the predicted values from the final target classifier and the base classifiers are similar for the unlabeled target data. Experiments are performed on the applications of video concept detection, text classification, and email spam detection. The methods tested against include a baseline approach using an SVM classifier trained on the labeled source data, the feature replication method from Daumé BIB007 , an adaptive SVM method from Yang BIB008 , a cross-domain SVM method proposed by Jiang BIB011 , and a kernel mean matching method by Huang BIB005 . The DTMKL approach uses an SVM learner for the experiments. Average precision and classification accuracy are measured as the performance metrics. The DTMKL method performed the best for all applications, and the baseline approach is consistently the worst performing. The other methods showed better performance over the baseline which demonstrated a positive transfer learning effect. The work by Long BIB030 is a joint domain adaptation (JDA) solution that aims to simultaneously correct for the marginal and conditional distribution differences between the labeled source domain and the unlabeled target domain. Principal component analysis (PCA) is used for optimization and dimensionality reduction. To address the difference in marginal distribution between the domains, the maximum mean discrepancy distance measure BIB006 is used to compute the marginal distribution differences and is integrated into the PCA optimization algorithm. The next part of the solution requires a process to correct the conditional distribution differences, which requires labeled target data. Since the target data is unlabeled, pseudo labels (estimated target labels) are found by learning a classifier from the labeled source data. The maximum mean discrepancy distance measure is modified to measure the distance between the conditional distributions and is integrated into the PCA optimization algorithm to minimize the conditional distributions. Finally, the features identified by the modified PCA algorithm are used to train the final target classifier. Experiments are performed for the application of image recognition and classification accuracy is measured as the performance metric. Two baseline approaches of a 1-nearest neighbor classifier and a PCA approach trained on the source data are tested. Transfer learning approaches tested for this experiment include the approach by Pan BIB020 , Gong BIB026 , and Si BIB015 . These transfer learning approaches only attempt to correct for marginal distribution differences between domains. The Long BIB030 approach is the best performing, followed by the Pan BIB020 and Si BIB015 approaches (a tie), then the Gong BIB026 approach, and finally the baseline approaches. All transfer learning approaches perform better than the baseline approaches. The possible reason behind the underperformance of the Gong BIB026 approach is the data smoothness assumption that is made for the Gong BIB026 solution may not be intact for the data sets tested. The paper by Long BIB032 proposes an Adaptation Regularization based transfer learning (ARTL) framework for scenarios of labeled source data and unlabeled target data. This transfer learning framework proposes to correct the difference in marginal distribution between the source and target domains, correct the difference in conditional distribution between the domains, and improve classification performance through a manifold regularization  process (which optimally shifts the hyperplane of an SVM learner). This complete framework process is depicted in Fig. 2 . The proposed ARTL framework will learn a classifier by simultaneously performing structural risk minimization BIB001 , reducing the marginal and conditional distributions between the domains, and optimizing the manifold consistency of the marginal distribution. To resolve the conditional distribution differences, pseudo labels are found for the target data in the same way as proposed by Long BIB030 . A difference between the ARTL approach and Long BIB030 is ARTL learns the final classifier simultaneously while minimizing the domain distribution differences, which is claimed by Long BIB032 to be a more optimal solution. Unfortunately, the solution by Long BIB030 is not included in the experiments. Experiments are performed on the applications of text classification and image classification where classification accuracy is measured as the performance metric. There are three baseline methods tested where different classifiers are trained with the labeled source data. There are five transfer learning methods tested against, which include methods by Ling BIB012 , Pan BIB016 , Pan BIB020 , Quanz BIB014 , and Xiao BIB027 . The order of performance from best to worst is ARTL, Xiao BIB027 , Pan BIB020 , Pan BIB016 , Quanz BIB014 and Ling BIB012 (tie), and the baseline approaches. The baseline methods underperformed all other transfer learning approaches tested. The work of Kulis BIB021 , referred to as the asymmetric regularized cross-domain Transformation (ARC-t), proposes an asymmetric transformation algorithm to resolve the heterogeneous feature space between domains. For this scenario, there is an abundance of labeled source data and limited labeled target data. An objective function is first defined for learning the transformation matrix. The objective function contains a regularizer term and a cost function term that is applied to each pair of cross-domain instances and the learned transformation matrix. The construction of the objective function is responsible for the domain invariant transformation process. The optimization of the objective function aims to minimize the regularizer and the cost function terms. The transformation matrix is learned in a non-linear Gaussian RBF kernel space. The method presented is referred to as the asymmetric regularized cross-domain transformation. Two experiments using this approach are performed for image classification where classification accuracy is measured as the performance metric. There are 31 image classes defined for these experiments. The first experiment (test 1) is where instances of all 31 image classes are included in the source and target training data. In the second experiment (test 2), only 16 image classes are represented in the target training data (all 31 are represented in the source). To test against other baseline approaches, a method is needed to bring the source and target input domains together. A preprocessing step called Kernel Canonical Correlation Analysis (proposed by Shawe-Taylor BIB003 ) is used to project the source and target domains into a common domain space using symmetric transformation. Baseline approaches tested include k-nearest neighbors, SVM, metric learning proposed by Davis BIB009 , feature augmentation proposed by Daumé BIB007 , and a cross domain metric learning method proposed by Saenko  . For test 1, the Kulis BIB021 approach performs marginally better than the other methods tested. For test 2, the Fig. 3 Depicts algorithm approach by Li BIB033 where the heterogeneous source and target features are transformed to an augmented latent feature space. T S and T T are transformation functions. P and Q are projection matrices as described in Duan BIB028 Diagram adapted from Li BIB033 Kulis BIB021 approach performs significantly better compared to the k-nearest neighbors approach (note the other methods cannot be tested against as they require all 31 classes to be represented in the target training data). The Kulis BIB021 approach is best suited for scenarios where all of the classes are not represented in the target training data as demonstrated in test 2. The problem domain defined by Harel BIB017 is of limited labeled target data and multiple labeled data sources where an asymmetric transformation is desired for each source to resolve the mismatch in feature space. The first step in the process is to normalize the features in the source and target domains, then group the instances by class in the source and target domains. For each class grouping, the features are mean adjusted to zero. Next, each individual source class group is paired with the corresponding target class group, and a singular value decomposition process is performed to find the specific transformation matrix for that class grouping. Once the transformation is performed, the features are mean shifted back reversing the previous step, and the final target classifier is trained using the transformed data. Finding the transformation matrix using the singular value decomposition process allows for the marginal distributions within the class groupings to be aligned while maintaining the structure of the data. This approach is referred to as the Multiple Outlook MAPping algorithm (MOMAP). The experiments use data taken from wearable sensors for the application of activity classification. There are five different activities defined for the experiment which include walking, running, going upstairs, going downstairs, and lingering. The source domain contains similar (but different) sensor readings as compared to the target. The method proposed by Harel BIB017 is compared against a baseline method that trains a classifier with the limited labeled target data and an upper bound method that uses a significantly larger set of labeled target data to train a classifier. An SVM learner is used as the base classifier and a balanced error rate (due to an imbalance in the test data) is measured as the performance metric. The Harel BIB017 approach outperforms the baseline method in every test and falls short of the upper bound method in every test with respect to the balanced error rate. The heterogeneous transfer learning scenario addressed by Zhou BIB034 requires an abundance of labeled source data and limited labeled target data. An asymmetric transformation function is proposed to map the source features to the target features. To learn the transformation matrix, a multi-task learning method based on Ando BIB004 is adopted. The solution, referred to as the sparse heterogeneous feature representation (SHFR), is implemented by creating a binary classifier for each class in the source and the target domains separately. Each binary classifier is assigned a weight term where the weight terms are learned by combining the weighted classifier outputs, while minimizing the classification error of each domain. The weight terms are now used to find the transformation matrix by minimizing the difference between the target weights and the transformed source weights. The final target classifier is trained using the transformed source data and original target data. Experiments are performed for text document classification where the target domain contains documents written in one language and the source domain contains documents written in different languages. A baseline method using a linear SVM classifier trained on the labeled target is established along with testing against the methods proposed by Wang BIB022 , Kulis BIB021 , and Duan BIB028 . The method proposed by Zhou BIB034 performed the best for all tests with respect to classification accuracy. The results of the other approaches are mixed as a function of the data sets used where the Duan BIB028 method performed either second or third best. The application of software module defect prediction is usually addressed by training a classifier with labeled data taken from the software project of interest. The environment described in Nam BIB037 for software module defect prediction attempts to use labeled source data from one software project to train a classifier to predict unlabeled target data from another project. The source and target software projects collect different metrics making the source and target feature spaces heterogeneous. The proposed solution, referred to as the heterogeneous defect prediction (HDP) approach, is to first select the important features from the source domain using a feature selection method to eliminate redundant and irrelevant features. Feature selection methods used include gain ratio, Chi square, relief-F, and significance attribute evaluation (see Gao BIB023 and Shivaji BIB031 ). The next step is to statistically match the selected source domain features to ones in the target using a Kolmogorov-Smirnov test that measures the closeness of the empirical distribution between the two sources. A learner is trained with the source features that exhibit a close statistical match to the corresponding target features. The target data is tested with the trained classifier using the corresponding matched features of the target. Even though the approach by Nam BIB037 is applied directly to the application of software module defect prediction, this method can be used for other applications. Experiments are performed using five different software defect data sets with heterogeneous features. The proposed method by Nam BIB037 uses logistic regression as the base learner. The other approaches tested include a within project defect prediction (WPDP) approach where the learner is trained on labeled target data, a cross project defect prediction (CPDP-CM) approach where the source and target represent different software projects but have homogeneous features, and a cross project defect prediction approach with heterogeneous features (CPDP-IFS) as proposed by He BIB035 . The results of the experiment show the Nam BIB037 method significantly outperformed all other approaches with respect to area under the curve measurement. The WPDP approach is next best followed by the CPDP-CM approach and the CPDP-IFS approach. These results can be misleading as the Nam BIB037 approach could only match at least one or more input features between the source and target domains in 37 % of the tests. Therefore, in 63 % of the cases, the Nam BIB037 method could not be used and these cases are not counted. The WPDP method represents an upper bound and it is an unexpected result that the Nam BIB037 approach would outperform the WPDP method. The paper by Zhou BIB036 claims that previous heterogeneous solutions assume the instance correspondence between the source and target domains are statistically representative (distributions are equal), which may not always be the case. An example of this claim is in the application of text sentiment classification where the word bias problem previously discussed causes distribution differences between the source and target domains. The paper by Zhou BIB036 proposes a solution called the hybrid heterogeneous transfer learning (HHTL) method for a heterogeneous environment with abundant labeled source data and abundant unlabeled target data. The idea is to first learn an asymmetric transformation from the target to the source domain, which reduces the problem to a homogeneous domain adaptation issue. The next step is to discover a common latent feature space using the transformed data (from the previous step) to reduce the distribution bias between the transformed unlabeled target domain and the labeled source domain. Finally, a classifier is trained using the common latent feature space from the labeled source data. This solution is realized using a deep learning method employing a marginalized stacked denoised autoencoder as proposed by Chen BIB029 to learn the asymmetric transformation and the mapping to a common latent feature space. The previous surveyed paper by Glorot [41] demonstrated a deep learning approach finding a common latent feature space for homogeneous source and target feature set. The experiments focused on multiple language text sentiment classification where English is used in the source and three other languages are separately used in the target. Classification accuracy is measured as the performance metric. Other methods tested include a heterogeneous spectral mapping approach proposed by Shi BIB018 , a method proposed by Vinokourov BIB002 , and a multimodal deep learning approach proposed by Ngiam BIB024 . An SVM learner is used as the base classifier for all methods. The results of the experiment from best to worst performance are Zhou BIB036 , Ngiam BIB024 , Vinokourov BIB002 , and Shi BIB018 .