Generalized multi-view learning based on generalized eigenvalues proximal support vector machines
作者：
Highlights：
• We propose two new general multi-view learning models.
• They fully utilize the consensus and complementarity principle among views.
• They are extended to nonlinear cases by the kernel trick.
• An iterative algorithm is developed to obtain the solution.
• Experimental results validate the effectiveness of the proposed methods.
摘要
•We propose two new general multi-view learning models.•They fully utilize the consensus and complementarity principle among views.•They are extended to nonlinear cases by the kernel trick.•An iterative algorithm is developed to obtain the solution.•Experimental results validate the effectiveness of the proposed methods.
论文关键词：Multi-view learning,Generalized eigenvalue proximal support vector machines,Multi-view co-regularization,Consistency information
论文评审过程：Received 12 May 2020, Revised 30 December 2021, Accepted 31 December 2021, Available online 15 January 2022, Version of Record 21 January 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.116491
