Robust supervised multi-view feature selection with weighted shared loss and maximum margin criterion
作者：
Highlights：
• A multi-view feature selection based on weighted shared loss and MMC is proposed.
• Weighted shared loss is designed to maintain the complementary information of views.
• Weighted view-based MMC regularizer is designed to learn view’s structure knowledge.
• The proposed algorithm realizes the view-block calculation via ADMM.
• The extensive experiments demonstrate the effectiveness of the proposed method.
摘要
•A multi-view feature selection based on weighted shared loss and MMC is proposed.•Weighted shared loss is designed to maintain the complementary information of views.•Weighted view-based MMC regularizer is designed to learn view’s structure knowledge.•The proposed algorithm realizes the view-block calculation via ADMM.•The extensive experiments demonstrate the effectiveness of the proposed method.
论文关键词：Multi-view learning,Weighted shared loss,Maximum margin criterion,Robust,Feature selection
论文评审过程：Received 11 December 2020, Revised 24 June 2021, Accepted 20 July 2021, Available online 27 July 2021, Version of Record 3 August 2021.
论文官网地址：https://doi.org/10.1016/j.knosys.2021.107331
