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We propose a new learning method for heterogeneous domain adaptation (HDA),in which the data from the source domain and the target domain are representedby heterogeneous features with different dimensions. Using two differentprojection matrices, we first transform the data from two domains into a commonsubspace in order to measure the similarity between the data from two domains.We then propose two new feature mapping functions to augment the transformeddata with their original features and zeros. The existing learning methods(e.g., SVM and SVR) can be readily incorporated with our newly proposedaugmented feature representations to effectively utilize the data from bothdomains for HDA. Using the hinge loss function in SVM as an example, weintroduce the detailed objective function in our method called HeterogeneousFeature Augmentation (HFA) for a linear case and also describe itskernelization in order to efficiently cope with the data with very highdimensions. Moreover, we also develop an alternating optimization algorithm toeffectively solve the nontrivial optimization problem in our HFA method.Comprehensive experiments on two benchmark datasets clearly demonstrate thatHFA outperforms the existing HDA methods.

Learning with Augmented Features for Heterogeneous Domain Adaptation