Heterogeneous domain adaptation for multiple classes

Publication Type:
Journal Article
Journal of Machine Learning Research, 2014, 33 pp. 1095 - 1103
Issue Date:
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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.
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