A survey on heterogeneous transfer learning <s> HDCC <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> BIB001 </s> A survey on heterogeneous transfer learning <s> HDCC <s> In this study, the authors study the video annotation problem over heterogeneous domains, in which data from the image source domain and the video target domain is represented by heterogeneous features with different dimensions and physical meanings. A novel feature learning method, called heterogeneous discriminative analysis of canonical correlation (HDCC), is proposed to discover a common feature subspace in which heterogeneous features can be compared. The HDCC utilises discriminative information from the source domain as well as topology information from the target domain to learn two different projection matrices. By using these two matrices, heterogeneous data can be projected onto a common subspace and different features can be compared. They additionally design a group weighting learning framework for multi-domain adaptation to effectively leverage knowledge learned from the source domain. Under this framework, source domain images are organised in groups according to their semantic meanings, and different weights are assigned to these groups according to their relevancies to the target domain videos. Extensive experiments on the Columbia Consumer Video and Kodak datasets demonstrate the effectiveness of their HDCC and group weighting methods. <s> BIB002
Wang et al. BIB002 proposed a framework called Heterogeneous Discriminative Analysis of Canonical Correlation (HDCC) to handle HDA for the application of video annotation, as well as other HTL, tasks. For video annotation tasks, it is often expensive and difficult to collect sufficient labeled videos to build an effective classifier. The goal, in this case, is to use transfer learning to transfer knowledge from a label abundant image source domain to enhance a target video annotation task which, in this case, has no labels. The issue becomes that source image data are represented by static image features while target video data are represented by spatial-temporal video features. Thus, this method adopts Canonical Correlation Analysis (CCA) to create a common subspace using projection matrices for the source and target by incorporating both the discriminative information exhibited in the source, and the topology information in the target. The topology information in the target is explored by taking each target video and splitting it into smaller clips where then a frame is selected randomly, thus creating a static image from the video as to relate to the static source images. The discriminative information is extracted from the source by maximizing the similarity (reducing the variance) of infra-class samples while minimizing the similarities (maximizing the variance) of inter-class samples to create a more discriminative space. From this subspace, we can then apply standard learning algorithms to create a target model. In this work, DASVM BIB001 is used due to the distribution differences between the captured keyframe and the source image. This method initializes the model using the source labeled data and then they are gradually replaced by target instances to learn the final separation hyperplane. Wang et al. BIB002 also extends HDCC by introducing a joint group weighing methodology to learn from multiple heterogeneous sources. The idea behind this is to organize source images into groups in a joint manner based on their semantic meanings rather than origin. Then, weights are assigned to each group of input source data based on their relatedness to the target video. This in turn, allows for selection of more performance enhancing source data which contains greater semantic knowledge for transferring to the target task. The training with these groups can be split into two phases: (1) a classifier is learned for each input source group and (2) weights are assigned and optimization of such generate the target classifier. In the test phase, image frames are extracted from the videos and inputted into the classifier to predict a final label. Experiments were performed for image-aided video annotation which demonstrated the effectiveness of the proposed HDCC and group weighing methods.