Heterogeneous domain adaptation with label and structure consistency

Domain adaptation is a challenging task, since it associates data collected from different domains or exhibiting distinct distributions. In this paper, we particularly focus on adapting cross-domain data with distinct feature dimensions or representations. Thus, this is referred to as the task of heterogeneous domain adaptation (HDA). To solve HDA, we propose Label and Structure-consistent Unilateral Projection (LS-UP) that transforms source-domain data to the target domain, with the goal of matching cross-domain data distribution and preserving data structure after projection. The main contribution of our work is its ability in relating cross-domain data with different feature representations. We evaluate our LS-UP for HDA on two different cross-domain classification problems, and we show that our method would perform favorably against state-of-the-art approaches.