Discriminative Transfer Learning on Manifold

Collective matrix factorization has achieved a remarkable success in document classification in the literature of transfer learning. However, the learned latent factors still suffer from the divergence between different domains and thus are usually not discriminative for an appropriate assignment of category labels. Based on these observations, we impose a discriminative regression model over the latent factors to enhance the capability of label prediction. Moreover, we propose to minimize the Maximum Mean Discrepancy in the latent manifold subspace, as opposed to typically in the original data space, to bridge the gap between different domains. Specifically, we formulate these objectives into a joint optimization framework with two matrix tri-factorizations for the source and target domains simultaneously. An iterative algorithm DTLM is developed and the theoretical analysis of its convergence is discussed. Empirical study on benchmark datasets validates that DTLM improves the classification accuracy consistently compared with the state-of-theart transfer learning methods.