Distribution matching and structure preservation for domain adaptation

Abstract Cross-domain classification refers to completing the corresponding classification task in a target domain which lacks label information, by exploring useful knowledge in a related source domain but with different data distribution. Domain adaptation can deal with such cross-domain classification, by reducing divergence of domains and transferring the relevant knowledge from the source to the target. To mine the discriminant information of the source domain samples and the geometric structure information of domains, and thus improve domain adaptation performance, this paper proposes a novel method involving distribution matching and structure preservation for domain adaptation (DMSP). First, it aligns the subspaces of the source domain and target domain on the Grassmann manifold; and learns the non-distorted embedded feature representations of the two domains. Second, in this embedded feature space, the empirical structure risk minimization method with distribution adaptation regularization and intra-domain graph regularization is used to learn an adaptive classifier, further adapting the source and target domains. Finally, we perform extensive experiments on widely used cross-domain classification datasets to validate the superiority of DMSP. The average classification accuracy of DMSP on these datasets is the highest compared with several state-of-the-art domain adaptation methods.