Transfer metric learning for unsupervised domain adaptation

Domain adaptation is still a challenging task due to the fact that the distribution discrepancy between source domain and target domain weakens the transfer ability. Intuitively, it is crucial to discover a more discriminative feature representation across domains. However, previous methods do not take the target discriminative information into account since (most) target data are unlabelled. Here, the authors propose a transfer metric learning method which decreases intra-class distance and increases inter-class distance simultaneously even in the case of target data are unlabelled. The shared features are more discriminative, hence the model could be more robust for target data. Specially, the global optimal solution can be obtained by solving a generalised eigen-decomposition problem. Extensive experiments on image datasets demonstrate that compared to several state-of-the-art methods, authors’ method achieves significant improvement of 9.0% in average classification accuracy.