Discriminative transfer learning via local and global structure preservation

The current success of supervised learning is limited on large amounts of labeled training data. Transfer learning aims to learn an adaptive classifier for the unlabeled target domain data from the labeled source domain data, which is sampled from diverse probability distributions under changing conditions. Most previous works focus on how to reduce the distribution discrepancy between two involved domains, or exploit the shared common feature by preserving the local geometric structure of samples. In this paper, we propose a modified method jointly optimizing the local and global structure preservation. The main idea is to explore common features with manifold regularization. Discriminative repulsive force model is used to improve maximum mean discrepancy, which keeps discriminative property in the local sense via labeled source domain data and alleviates the global distribution discrepancy of the different domains. Quantitative results indicate that our method performs better than other methods on 16 cross-domain experiments.