Transferable Feature Selection for Unsupervised Domain Adaptation : Extended Abstract

Yuguang Yan, Hanrui Wu, Yuzhong Ye, Chaoyang Bi, Min Lu, Dapeng Liu, Qingyao Wu, Michael Kwok-Po Ng

Abstract: Domain adaptation aims at extracting knowledge from auxiliary source domains to assist the learning task in a target domain. Since the distributions of the source and target domains are different, directly using source data to build a classifier for the target domain may hamper the classification performance on the target data. In this paper, we propose to find a feature subset that is both transferable and discriminative, so that both the domain discrepancy and the classification loss measured on the selected features can be reduced. To achieve this, we formulate a new sparse learning model that is able to jointly reduce the domain discrepancy and select informative features for classification. Extensive experiments on real-world data sets demonstrate the effectiveness of the proposed method.