Multi-source adaptation embedding with feature selection by exploiting correlation information

While feature selection has recently received much research attention, less or limited effort has been made on improving the performance of feature selection by leveraging the shared knowledge from other related domains. Besides, multi-source adaptation embedding by exploiting the correlation information among domain features and distributions has long been largely unaddressed. To this end, we propose in this paper a robust Multi-source Adaptation Embedding framework with Feature Selection (MAEFS) by exploiting the correlation information via joint l(2, 1)-norm and trace-norm regularization, and apply it to cross-domain visual recognition. Specifically, to uncover cross-domain invariant subspaces by minimizing the distribution discrepancy between source and target domains, instead of evaluating the importance of each feature individually, MAEFS selects features in a collaborated mode for considering the correlation information among features. Furthermore, multiple feature selection functions for different source adaptation objects are simultaneously learned in a joint framework, which enables MAEFS to utilize the correlated knowledge among multiple source domains via trace-norm regularization, thus facilitating domain invariant embedding. Besides, by employing graph embedding and sparse regression scheme via l(2, 1)-norm minimization, MAEFS can preserve the original geometrical structure information as well as be robust to some noises or outliers existed in domains. Finally, an efficient iterative algorithm is proposed to optimize MAEFS, whose convergence is theoretically guaranteed. Comprehensive experimental evidence on a large number of visual datasets verifies the effectiveness of the proposed framework. (C) 2017 Elsevier B.V. All rights reserved.