Structured Multi-view Supervised Feature Selection Algorithm Research

Caijuan Shi, Li-li Zhao, Liping Liu, Jian Liu, Qi Tian

Abstract: Face more and more multi-view data, how to enhance the feature selection performance has become one of the research issues. However, the most existing multi-view feature selection methods only consider the importance of each view features, but ignore the importance of individual feature in each view in the feature selection progress. In this paper we propose a novel supervised feature selection method based on structured multi-view sparse regularization, namely Structured Multi-view Supervised Feature Selection (SMSFS). SMSFS can realize feature selection by both considering the importance of each view features and the importance of individual feature in each view to boost the feature selection performance. Extensive experiments are performed on two image datasets and the results show the effectiveness of the proposed method SMSFS.