Heterogeneous Domain Adaptation for Multiple Classes

In this paper, we present an efficient Multi-class Heterogeneous Domain Adaptation (HDA) method, where data from the source and target domains are represented by heterogeneous features with different dimensions. Specifically, we propose to reconstruct a sparse feature transformation matrix to map the features of multiple classes from the source domain to the target domain. We cast this learning task as a compressed sensing problem, where each classifier can be deemed as a measurement sensor. Based on compressive sensing theory, the estimation error of the transformation matrix decreases with the increasing number of classifiers. Therefore, to guarantee the reconstruction performance, we construct sufficiently many binary classifiers based on the error correcting output correcting. Extensive experiments are conducted on both toy data and three real-world HDA applications to verify the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy.