Transfer Domain Class Clustering for Unsupervised Domain Adaptation

In this paper, we propose a transfer domain class clustering (TDCC) algorithm to address the unsupervised domain adaptation problem, in which the training data (source domain) and the test data (target domain) follow different distributions. TDCC aims to derive new feature representations for source and target in a latent subspace to simultaneously reduce the distribution distance between two domains, which helps transfer the source knowledge to the target domain effectively, and enhance the class discriminativeness of data as much as possible by minimizing the intra-class variations, which can benefit the final classification a lot. The effectiveness of TDCC is verified by comprehensive experiments on several cross-domain datasets, and the results demonstrate that TDCC is superior to the competitive algorithms.