Multi-view discriminant transfer learning

We study to incorporate multiple views of data in a perceptive transfer learning framework and propose a Multi-view Discriminant Transfer (MDT) learning approach for domain adaptation. The main idea is to find the optimal discriminant weight vectors for each view such that the correlation between the two-view projected data is maximized, while both the domain discrepancy and the view disagreement are minimized simultaneously. Furthermore, we analyze MDT theoretically from discriminant analysis perspective to explain the condition and reason, under which the proposed method is not applicable. The analytical results allow us to investigate whether there exist within-view and/or between-view conflicts, and thus provides a deep insight into whether the transfer learning algorithm work properly or not in the view-based problems and the combined learning problem. Experiments show that MDT significantly outperforms the state-of-the-art baselines including some typical multi-view learning approaches in single-or cross-domain.