Incremental Dictionary Learning for Unsupervised Domain Adaptation

Domain adaptation (DA) methods attempt to solve the domain mismatch problem between source and target data. In this paper, we propose an incremental dictionary learning method where some target data called supportive samples are selected to assist adaptation. Supportive samples are close to the source domain and have two properties: first, their predicted class labels are reliable and can be used for building more discriminative classification models; second, they act as a bridge to connect the two domains and reduce the domain mismatch. Theoretical analysis shows that both properties are important for adaptation, enabling the idea of adding supportive samples to the source domain. A stopping criterion is designed to guarantee that the domain mismatch decreases monotonically during adaptation. Experimental results on several widely used visual datasets show that the proposed approach performs better than many state-of-the-art methods.