Manifold transfer subspace learning based on double relaxed discriminative regression

By leveraging the labeled data samples of the source domain to learn the unlabeled data samples of the target domain, unsupervised domain adaptation (DA) has achieved promising performance. However, it is still a vital problem for unsupervised domain adaptation to deal with cross-domain distribution mismatch. Therefore, we present a new model framework for cross-domain image classification in the paper, which is termed manifold transfer subspace learning based on double relaxed discriminative regression (MTSL-DRDR). First, the global geometry information of the samples from the source and target domain can be preserved by utilizing the low-rank constraint. Second, the two transformation projections are employed to project both domains to a unified subspace, in which each data sample of the target domain can be represented by some samples from the source domain with the sparse and low-rank coefficient matrix. Third, the local structure information of the data points with the same semantics from the different domains is preserved by means of the adaptive weight graph based on the low-rank coefficient matrix. Last, for fully use the discriminative information of data from the source domain, the discriminant information of the source domain based on intra-class and inter-class graphs is encoded to the target domain. Our MTSL-DRDR algorithm is evaluated on challenging benchmark datasets, and a large number of experiment results show the superiority of the proposed method.