A survey of transfer learning <s> Negative transfer <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> BIB001 </s> A survey of transfer learning <s> Negative transfer <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> BIB002 </s> A survey of transfer learning <s> Negative transfer <s> In this paper, we propose a novel graph-based method for knowledge transfer. We model the transfer relationships between source tasks by embedding the set of learned source models in a graph using transferability as the metric. Transfer to a new problem proceeds by mapping the problem into the graph, then learning a function on this graph that automatically determines the parameters to transfer to the new learning task. This method is analogous to inductive transfer along a manifold that captures the transfer relationships between the tasks. We demonstrate improved transfer performance using this method against existing approaches in several real-world domains. <s> BIB003 </s> A survey of transfer learning <s> Negative transfer <s> The effectiveness of knowledge transfer using classification algorithms depends on the difference between the distribution that generates the training examples and the one from which test examples are to be drawn. The task can be especially difficult when the training examples are from one or several domains different from the test domain. In this paper, we propose a locally weighted ensemble framework to combine multiple models for transfer learning, where the weights are dynamically assigned according to a model's predictive power on each test example. It can integrate the advantages of various learning algorithms and the labeled information from multiple training domains into one unified classification model, which can then be applied on a different domain. Importantly, different from many previously proposed methods, none of the base learning method is required to be specifically designed for transfer learning. We show the optimality of a locally weighted ensemble framework as a general approach to combine multiple models for domain transfer. We then propose an implementation of the local weight assignments by mapping the structures of a model onto the structures of the test domain, and then weighting each model locally according to its consistency with the neighborhood structure around the test example. Experimental results on text classification, spam filtering and intrusion detection data sets demonstrate significant improvements in classification accuracy gained by the framework. On a transfer learning task of newsgroup message categorization, the proposed locally weighted ensemble framework achieves 97% accuracy when the best single model predicts correctly only on 73% of the test examples. In summary, the improvement in accuracy is over 10% and up to 30% across different problems. <s> BIB004 </s> A survey of transfer learning <s> Negative transfer <s> Recent years have witnessed an increased interest in transfer learning. Despite the vast amount of research performed in this field, there are remaining challenges in applying the knowledge learnt from multiple source domains to a target domain. First, data from multiple source domains can be semantically related, but have different distributions. It is not clear how to exploit the distribution differences among multiple source domains to boost the learning performance in a target domain. Second, many real-world applications demand this transfer learning to be performed in a distributed manner. To meet these challenges, we propose a consensus regularization framework for transfer learning from multiple source domains to a target domain. In this framework, a local classifier is trained by considering both local data available in a source domain and the prediction consensus with the classifiers from other source domains. In addition, the training algorithm can be implemented in a distributed manner, in which all the source-domains are treated as slave nodes and the target domain is used as the master node. To combine the training results from multiple source domains, it only needs share some statistical data rather than the full contents of their labeled data. This can modestly relieve the privacy concerns and avoid the need to upload all data to a central location. Finally, our experimental results show the effectiveness of our consensus regularization learning. <s> BIB005 </s> A survey of transfer learning <s> Negative transfer <s> Ensemble classifiers such as bagging, boosting and model averaging are known to have improved accuracy and robustness over a single model. Their potential, however, is limited in applications which have no access to raw data but to the meta-level model output. In this paper, we study ensemble learning with output from multiple supervised and unsupervised models, a topic where little work has been done. Although unsupervised models, such as clustering, do not directly generate label prediction for each individual, they provide useful constraints for the joint prediction of a set of related objects. We propose to consolidate a classification solution by maximizing the consensus among both supervised predictions and unsupervised constraints. We cast this ensemble task as an optimization problem on a bipartite graph, where the objective function favors the smoothness of the prediction over the graph, as well as penalizing deviations from the initial labeling provided by supervised models. We solve this problem through iterative propagation of probability estimates among neighboring nodes. Our method can also be interpreted as conducting a constrained embedding in a transformed space, or a ranking on the graph. Experimental results on three real applications demonstrate the benefits of the proposed method over existing alternatives1. <s> BIB006 </s> A survey of transfer learning <s> Negative transfer <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> BIB007 </s> A survey of transfer learning <s> Negative transfer <s> This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available only for a source domain different (even if related) from the target domain of (unlabeled) test data. Two main novel contributions are proposed: 1) a domain adaptation support vector machine (DASVM) technique which extends the formulation of support vector machines (SVMs) to the domain adaptation framework and 2) a circular indirect accuracy assessment strategy for validating the learning of domain adaptation classifiers when no true labels for the target--domain instances are available. Experimental results, obtained on a series of two-dimensional toy problems and on two real data sets related to brain computer interface and remote sensing applications, confirmed the effectiveness and the reliability of both the DASVM technique and the proposed circular validation strategy. <s> BIB008 </s> A survey of transfer learning <s> Negative transfer <s> We consider the characterization of muscle fatigue through noninvasive sensing mechanism such as surface electromyography (SEMG). While changes in the properties of SEMG signals with respect to muscle fatigue have been reported in the literature, the large variation in these signals across different individuals makes the task of modeling and classification of SEMG signals challenging. Indeed, the variation in SEMG parameters from subject to subject creates differences in the data distribution. In this paper, we propose a transfer learning framework based on the multi-source domain adaptation methodology for detecting different stages of fatigue using SEMG signals, that addresses the distribution differences. In the proposed framework, the SEMG data of a subject represent a domain; data from multiple subjects in the training set form the multiple source domains and the test subject data form the target domain. SEMG signals are predominantly different in conditional probability distribution across subjects. The key feature of the proposed framework is a novel weighting scheme that addresses the conditional probability distribution differences across multiple domains (subjects). We have validated the proposed framework on Surface Electromyogram signals collected from 8 people during a fatigue-causing repetitive gripping activity. Comprehensive experiments on the SEMG data set demonstrate that the proposed method improves the classification accuracy by 20% to 30% over the cases without any domain adaptation method and by 13% to 30% over the existing state-of-the-art domain adaptation methods. <s> BIB009 </s> A survey of transfer learning <s> Negative transfer <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> BIB010 </s> A survey of transfer learning <s> Negative transfer <s> Recent work has demonstrated the effectiveness of domain adaptation methods for computer vision applications. In this work, we propose a new multiple source domain adaptation method called Domain Selection Machine (DSM) for event recognition in consumer videos by leveraging a large number of loosely labeled web images from different sources (e.g., Flickr.com and Photosig.com), in which there are no labeled consumer videos. Specifically, we first train a set of SVM classifiers (referred to as source classifiers) by using the SIFT features of web images from different source domains. We propose a new parametric target decision function to effectively integrate the static SIFT features from web images/video keyframes and the spacetime (ST) features from consumer videos. In order to select the most relevant source domains, we further introduce a new data-dependent regularizer into the objective of Support Vector Regression (SVR) using the ∊-insensitive loss, which enforces the target classifier shares similar decision values on the unlabeled consumer videos with the selected source classifiers. Moreover, we develop an alternating optimization algorithm to iteratively solve the target decision function and a domain selection vector which indicates the most relevant source domains. Extensive experiments on three real-world datasets demonstrate the effectiveness of our proposed method DSM over the state-of-the-art by a performance gain up to 46.41%. <s> BIB011 </s> A survey of transfer learning <s> Negative transfer <s> Eigenvalues and the Laplacian of a graph Isoperimetric problems Diameters and eigenvalues Paths, flows, and routing Eigenvalues and quasi-randomness Expanders and explicit constructions Eigenvalues of symmetrical graphs Eigenvalues of subgraphs with boundary conditions Harnack inequalities Heat kernels Sobolev inequalities Advanced techniques for random walks on graphs Bibliography Index. <s> BIB012 </s> A survey of transfer learning <s> Negative transfer <s> Domain adaptation (DA), which leverages labeled data from related source domains, comes in handy when the label information of the target domain is scarce or unavailable. However, as the source data do not come from the same origin as that of the target domain, the predictive distributions of the source and target domains are likely to differ in reality. At the extreme, the predictive distributions of the source domains can differ completely from that of the target domain. In such case, using the learned source classifier to assist in the prediction of target data can result in prediction performance that is poorer than that with the omission of the source data. This phenomenon is established as negative transfer with impact known to be more severe in the multiclass context. To combat negative transfer due to differing predictive distributions across domains, we first introduce the notion of positive transferability for the assessment of synergy between the source and target domains in their prediction models, and we also propose a criterion to measure the positive transferability between sample pairs of different domains in terms of their prediction distributions. With the new measure, a predictive distribution matching (PDM) regularizer and a PDM framework learn the target classifier by favoring source data with large positive transferability while inferring the labels of target unlabeled data. Extensive experiments are conducted to validate the performance efficacy of the proposed PDM framework using several commonly used multidomain benchmark data sets, including Sentiment, Reuters, and Newsgroup, in the context of both binary-class and multiclass domains. Subsequently, the PDM framework is put to work on a real-world scenario pertaining to water cluster molecule identification. The experimental results illustrate the adverse impact of negative transfer on several state-of-the-art DA methods, whereas the proposed framework exhibits excellent and robust predictive performances. <s> BIB013 </s> A survey of transfer learning <s> Negative transfer <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> BIB014 </s> A survey of transfer learning <s> Negative transfer <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> BIB015 </s> A survey of transfer learning <s> Negative transfer <s> Transfer learning has benefited many real-world applications where labeled data are abundant in source domains but scarce in the target domain. As there are usually multiple relevant domains where knowledge can be transferred, multiple source transfer learning MSTL has recently attracted much attention. However, we are facing two major challenges when applying MSTL. First, without knowledge about the difference between source and target domains, negative transfer occurs when knowledge is transferred from highly irrelevant sources. Second, existence of imbalanced distributions in classes, where examples in one class dominate, can lead to improper judgement on the source domains' relevance to the target task. Since existing MSTL methods are usually designed to transfer from relevant sources with balanced distributions, they will fail in applications where these two challenges persist. In this article, we propose a novel two-phase framework to effectively transfer knowledge from multiple sources even when there exists irrelevant sources and imbalanced class distributions. First, an effective supervised local weight scheme is proposed to assign a proper weight to each source domain's classifier based on its ability of predicting accurately on each local region of the target domain. The second phase then learns a classifier for the target domain by solving an optimization problem which concerns both training error minimization and consistency with weighted predictions gained from source domains. A theoretical analysis shows that as the number of source domains increases, the probability that the proposed approach has an error greater than a bound is becoming exponentially small. We further extend the proposed approach to an online processing scenario to conduct transfer learning on continuously arriving data. Extensive experiments on disease prediction, spam filtering and intrusion detection datasets demonstrate that: i the proposed two-phase approach outperforms existing MSTL approaches due to its ability of tackling negative transfer and imbalanced distribution challenges, and ii the proposed online approach achieves comparable performance to the offline scheme. <s> BIB016 </s> A survey of transfer learning <s> Negative transfer <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> BIB017 </s> A survey of transfer learning <s> Negative transfer <s> One of the main research problems in heterogeneous transfer learning is to determine whether a given source domain is effective in transferring knowledge to a target domain, and then to determine how much of the knowledge should be transferred from a source domain to a target domain. The main objective of this paper is to solve this problem by evaluating the relatedness among given domains through transferred weights. We propose a novel method to learn such transferred weights with the aid of co-occurrence data, which contain the same set of instances but in different feature spaces. Because instances with the same category should have similar features, our method is to compute their principal components in each feature space such that co-occurrence data can be rerepresented by these principal components. The principal component coefficients from different feature spaces for the same instance in the co-occurrence data have the same order of significance for describing the category information. By using these principal component coefficients, the Markov Chain Monte Carlo method is employed to construct a directed cyclic network where each node is a domain and each edge weight is the conditional dependence from one domain to another domain. Here, the edge weight of the network can be employed as the transferred weight from a source domain to a target domain. The weight values can be taken as a prior for setting parameters in the existing heterogeneous transfer learning methods to control the amount of knowledge transferred from a source domain to a target domain. The experimental results on synthetic and real-world data sets are reported to illustrate the effectiveness of the proposed method that can capture strong or weak relations among feature spaces, and enhance the learning performance of heterogeneous transfer learning. <s> BIB018
The high-level concept of transfer learning is to improve a target learner by using data from a related source domain. But what happens if the source domain is not well-related to the target? In this case, the target learner can be negatively impacted by this weak relation, which is referred to as negative transfer. In a big data environment, there may be a large dataset where only a portion of the data is related to a target domain of interest. For this case, there is a need to divide the dataset into multiple sources and employ negative transfer methods when using transfer learning algorithm. In the scenario where multiple datasets are available that initially appear to be related to the target domain of interest, it is desired to select the datasets that provide the best information transfer and avoid the datasets that cause negative transfer. This allows for the best use of the available large datasets. How related do the source and target domains need to be for transfer learning to be advantageous? The area of negative transfer has not been widely researched, but the following papers begin to address this issue. Asymmetric feature Labeled Limited labels ✓ MOMAP BIB007 Asymmetric feature Labeled Limited labels ✓ SHFR BIB014 Asymmetric feature Labeled Limited labels ✓ HDP BIB017 Asymmetric feature Labeled Unlabeled ✓ HHTL BIB015 Asymmetric feature Labeled Unlabeled ✓ An early paper by Rosenstein  discusses the concept of negative transfer in transfer learning and claims that the source domain needs to be sufficiently related to the target domain; otherwise, the attempt to transfer knowledge from the source can have a negative impact on the target learner. Cases of negative transfer are demonstrated by Rosenstein  in experiments using a hierarchical Naive Bayes classifier. The author also demonstrates the chance of negative transfer goes down as the number of labeled target training samples goes up. The paper by Eaton BIB003 proposes to build a target learner based on a transferability measure from multiple related source domains. The approach first builds a logistic regression learner for each source domain. Next, a model transfer graph is constructed to represent the transferability between each source learner. In this case, transferability from a first learner to a second learner is defined as the performance of the second learner with learning from the first learner minus the performance of the second learner without learning from the first learner. Next, the model transfer graph is modified by adding the transferability measures between the target learner and all the source learners. Using spectral graph theory BIB012 on the model transfer graph, a transfer function is derived that maintains the geometry of the model transfer graph and is used in the final target learner to determine the level of transfer from each source. Experiments are performed in the applications of document classification and alphabet classification. Source domains are identified that are either related or unrelated to the target domain. The method by Eaton BIB003 is tested along with a handpicked method where the source domains are manually selected to be related to the target, an average method that uses all sources available, and a baseline method that does not use transfer learning. Classification accuracy is the performance metric measured in the experiments. The source and target domains are represented by a homogeneous feature input space. The results of the experiments are mixed. Overall, the Eaton BIB003 approach performs the best; however, there are certain instances where Eaton BIB003 performed worse than the handpicked, average, and baseline methods. In the implementation of the algorithm, the transferability measure between two sources is required to be the same; however, the transferability from source 1 to source 2 is not always equal to the transferability from source 2 to source 1. A suggestion for future improvement is to use directed graphs to specify the bidirectional nature of the transferability measure between two sources. The paper by Ge BIB016 claims that knowledge transfer can be inhibited due to the existence of unrelated or irrelevant source domains. Further, current transfer learning solutions are focused on transferring knowledge from source domains to a target domain, but are not concerned about different source domains that could potentially be irrelevant and cause negative transfer. In the model presented by Ge BIB016 , there is a single target domain with limited labeled data and multiple labeled source domains for knowledge transfer. To reduce negative transfer effects from unrelated source domains, each source is assigned a weight (called the Supervised Local Weight) corresponding to how related the source is with the target (the higher the weight the more it is related). The supervised local weight is found by first using a spectral clustering algorithm (Chung BIB012 ) on the unlabeled target information and propagating labels to the clusters from the labeled target information. Next, each source is separately clustered and labels assigned to the clusters from the labeled source. The supervised local weight of each source cluster is computed by comparing the source and target clusters. This solution further addresses the issue of imbalanced class distribution in source domains by preventing a high-weight class assignment in the case of high-accuracy predictions in a minority target class. The final target learner uses the supervised local weights to attenuate the effects of negative transfer. Experiments are performed in three application areas including cardiac arrhythmia detection, spam email filtering, and intrusion detection. Area under the curve is measured as the performance metric. The source and target domains are represented by a homogeneous feature input space. The method presented in this paper is compared against methods by Luo [71] , by Gao BIB006 , by Chattopadhyay BIB009 , and by Gao BIB004 . The Luo BIB005 and Gao BIB006 methods are the worst performing, most likely due to the fact that these solutions do not attempt to combat negative transfer effects. The Chattopadhyay BIB009 and Gao BIB004 methods are the next best performing, which have means in place to reduce the effects of negative transfer from the source domains. The Chattopadhyay BIB009 and Gao BIB004 methods do address the negative transfer problem but do not address the imbalanced distribution issue. The Ge BIB016 method does exhibit the best overall performance due to the handling of negative transfer and imbalanced class distribution. The paper by Seah BIB013 claims the root cause of negative transfer is mainly due to conditional distribution differences between source domains P S1 (y|x ) � = P S2 y|x and a difference in class distribution (class imbalance) between the source and target P S (y) � = P T y . Because the target domain usually contains a small number of labeled instances, it is difficult to find the true class distribution of the target domain. A predictive distribution matching (PDM) framework is proposed to align the conditional distributions of the source domains and target domain in an attempt to minimize negative transfer effects. A positive transferability measure is defined that measures the transferability of instance pairs with the same label from the source and target domains. The first step in the PDM framework is to assign pseudo labels to the unlabeled target data. This is accomplished by an iterative process that forces source and target instances which are similar (as defined by the positive transferability measure) to have the same label. Next, irrelevant source data are removed by identifying data that does not align with the conditional distribution of the pseudo labeled target data for each class. Both logistic regression and SVM classifiers are implemented using the PDM framework. Experiments are performed on document classification using the PDM method described in this paper, the approach from Daumé BIB002 , the approach from Huang BIB001 , and the approach from Bruzzone BIB008 . Classification accuracy is measured as the performance metric. The source and target domains are represented by a homogeneous feature input space. The PDM approach demonstrates better performance as compared to the other approaches tested as these solutions do not attempt to account for negative transfer effects. A select number of previously surveyed papers contain solutions addressing negative transfer. The paper by Yang BIB018 addresses the negative transfer issue, which is presented in the "Heterogeneous transfer learning" section. The homogeneous solution by Gong BIB010 defines an ROD value that measures the relatedness between a source and target domain. The work presented in Chattopadhyay BIB009 is a multiple source transfer learning approach that calculates the source weights as a function of conditional probability differences between the source and target domains attempting to give the most related sources the highest weights. Duan BIB011 proposes a transfer learning approach that only uses source domains that are deemed relevant and test data demonstrates better performance compared to methods with no negative transfer protection. The previous papers attempt to measure how related source data is to the target data in a transfer learning environment and then selectively transfer the information that is highly related. The experiments in the above papers demonstrate that accounting for negative transfer effects from source domain data can improve target learner performance. However, most transfer learning solutions do not attempt to account for negative transfer effects. Robust negative transfer measurements are difficult to define. Since the target domain typically has limited labeled data, it is inherently difficult to find a true measure of the relatedness between the source and target domains. Further, by selectively transferring information that seems related to the limited labeled target domain, a risk of overfitting in the target learner is a concern. The topic of negative transfer is a fertile area for further research.