Discovering and incorporating latent target-domains for domain adaptation

In this paper, we aim to address the unsupervised domain adaptation problem where the data in the target domain are much more diverse compared with the data in the source domain. In particular, this problem is formulated as discovering and incorporating latent domains underlying target data of interest for unsupervised domain adaptation. More specifically, the discovery of the latent target domains is based on three criteria, including the maximization of compactness and distinctiveness of the data in the individual latent target-domain, as well as the minimization of total divergence from the latent target-domains to the source domain. For each pair formed by a latent target domain and the source domain, we learn a feature space where the discrepancy between the source domain and the specific latent target domain is shrunk. Finally, we consider the projected source domain data on the learned latent feature spaces as different views of the source domain, and propose an extended multiple kernel learning algorithm to train a more robust and precise classifier for predicting the unlabeled target data. The effectiveness of our proposed method is demonstrated on various benchmark datasets for object recognition and human activity recognition. Moreover, we also show that our proposed method can be treated as an effective complement to the deep learning based unsupervised domain adaptation.