Semi-supervised feature selection analysis with structured multi-view sparse regularization

• Published in: Neurocomputing (Amsterdam) 2019-02, Vol.330, p.412-424
• Main Authors: Shi, Caijuan, Duan, Changyu, Gu, Zhibin, Tian, Qi, An, Gaoyun, Zhao, Ruizhen
• Format: Article
• Language: English
• Subjects: Multi-view Hessian regularization; Multi-view learning; Semi-supervised feature selection; Structured sparse regularization
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2018.10.027

•The structured multi-view sparse regularization is constructed.•Structured Multi-view Hessian sparse Feature Selection framework is proposed.•Multi-view Hessian regularization is utilized to enhance the performance.•A new iterative algorithm is introduced and its convergence is proven.•Experiments demonstrate SMHFS can effectively combine multi-view data information. Facing abundant and various multi-view data, how to effectively combine the multi-view data information has become an important research topic in feature selection analysis. However, existing feature selection methods usually consider each view features as a whole without fully considering the individual feature in each view. In this paper, we construct a structured multi-view sparse regularization and then propose a novel semi-supervise feature selection framework, namely Structured Multi-view Hessian sparse Feature Selection (SMHFS)11SMHFS: Structured Multi-view Hessian sparse Feature Selection.. With the structured multi-view sparse regularization, SMHFS can simultaneously learn the importance of each view features and the importance of individual feature in each view. In addition, SMHFS utilizes multi-view Hessian regularization to enhance the semi-supervised learning performance. An iterative algorithm is introduced and its convergence is proven. Finally, SMHFS is applied into image annotation task and extensive experiments are conducted. The experimental results show SMHFS can effectively combine the multi-view data information to achieve better feature selection performance compared to other methods.