A survey on heterogeneous transfer learning <s> HHTL <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> BIB001 </s> A survey on heterogeneous transfer learning <s> HHTL <s> Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to useful intermediate representations. We introduce and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern. This approach can be used to train autoencoders, and these denoising autoencoders can be stacked to initialize deep architectures. The algorithm can be motivated from a manifold learning and information theoretic perspective or from a generative model perspective. Comparative experiments clearly show the surprising advantage of corrupting the input of autoencoders on a pattern classification benchmark suite. <s> BIB002 </s> A survey on heterogeneous transfer learning <s> HHTL <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> BIB003 </s> A survey on heterogeneous transfer learning <s> HHTL <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> BIB004 </s> A survey on heterogeneous transfer learning <s> HHTL <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> BIB005 </s> A survey on heterogeneous transfer learning <s> HHTL <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> BIB006 </s> A survey on heterogeneous transfer learning <s> HHTL <s> Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. <s> BIB007
Zhou et al. BIB006 proposed a method for HTL called Hybrid Heterogeneous Transfer Learning (HHTL). This method creates an asymmetric mapping from the target to the source and takes into account bias issues of cross-domain correspondences. HHTL proposes using a Deep Learning BIB007 approach to perform cross-domain feature mapping and distribution bias reduction. It uses a labeled source, unlabeled target, and unlabeled correspondence data. When using correspondence data between source and target domains the method assumes that they are statistically representative (though this assumption may not always hold in real-world scenarios). An example of using correspondence data is when one has a cross-lingual, cross-domain review sentiment classification task in which we could use review-correspondences to learn a mapping between the two languages. The issue becomes that transforming the data from one language to another may not be effective due to distribution bias caused by the difference in product domains. Thus, this method aims to discover a latent feature representation to reduce such bias after transformation. To do so, this method proposes applying a marginalized Stacked Denoised Autoencoder (mSDA) BIB002 BIB005 on the source domain data with its corresponding unlabeled data and the target with its corresponding data as to learn high-level feature representations. Then from these high-level representations, a feature mapping is learned to create a latent common representation as to bridge the gap between heterogeneous feature spaces. These two steps of mSDA and heterogeneous feature mapping are recursively applied to the source and target data in each layer as to generate the different levels and feature transformations for the K layers used in this deep learning approach. After such process, standard classification models can be used on the source data with the latent representation to build a target classifier. Experiments were performed for multi-lingual sentiment classification with biased and unbiased correspondence instances. The proposed HHTL method was compared with HeMap BIB003 , multimodal deep learning BIB004 with CCA, cross-lingual KCCA BIB001 , and SVM-based method. This SVM-based method transfers labels predicted from the source correspondence data to the target as to train a target SVM on these pseudo-labels. Results showed the proposed method had the highest accuracy rates for the unbiased tests and for most cases in the biased tests.