Discriminative feature selection for multi-view cross-domain learning

Abstract

In many data mining applications, we often face the problem of cross-domain learning, i.e., to transfer the already learned knowledge from a source domain to a target domain. In particular, this problem becomes very challenging when there is no or little labeled training data available in the target domain, which is not an uncommon scenario as it is expensive and in certain cases even impossible to obtain any labeled training data in the target domain in many real world applications. In the literature, though few efforts are reported to attempt to solve this challenging problem, the solutions are all rather limited making this problem still open and challenging. On the other hand, as it is not uncommon to face this problem in many applications, an effective solution to this problem shall generate substantial societal impacts. In this paper, we address this problem and propose a new framework, called DISMUTE, taking advantage of the typically available multiple views of the data in domains. Consequently, DISMUTE is based on discriminative feature selection for multi-view cross-domain learning. Theoretic analysis and extensive evaluations in the specific application of object identification and image classification against several state-of-the-art methods demonstrate the outstanding superiority of DISMUTE.