Discrepancy-Aware Collaborative Representation for Unsupervised Domain Adaptation

Domain adaptation aims at learning from the la-beled source domain to build an accurate classifier for a related but different target domain. Existing methods attempt to reduce domain discrepancy explicitly by means of statistical properties yet ignore the inherent differences among samples. In this paper, we present a novel solution for domain adaptation based on collaborative representation, named Discrepancy-Aware Collaborative Representation (DACR). Inspired by the success of nearest regularization, DACR develops a novel indicator to measure the discrepancy among every source sample and target domain. Then the indicator is employed in sparse regularization thus ensure that samples with small discrepancy have larger weights in the learned representation. Extensive experiments verify that DACR is able to achieve comparable performance with existing methods while significantly reducing computing complexity.