A discriminative domain adaptation model for cross-domain image classification
Techniques of domain adaptation have been applied to address cross-domain recognition problems. In particular, such techniques favor the scenarios in which labeled data can be obtained at the source domain, but only few labeled target domain data are available during the training stage. In this paper, we propose a domain adaptation approach which is able to transfer source domain labeled data to the target domain, so that one can collect a sufficient amount of training data at that domain for recognition purposes. By advancing low-rank matrix decomposition for obtaining representative cross-domain data, our proposed model aims at transferring source domain labeled data to the target domain while preserving class label information. This introduces additional discriminating ability into our model, and thus improved recognition can be expected. Empirical results on cross-domain image datasets confirm the use of our proposed model for solving cross-domain recognition problems.