Primal explicit max margin feature selection for nonlinear support vector machines
作者：
Highlights：
• We solve a primal embedded feature selection problem for nonlinear SVMs.
• Use smooth hinge loss and trust region algorithm for bound constrained minimization.
• Propose alternating optimization approach to break problem into two smaller ones.
• Propose explicit margin maximization to solve feature selection subproblem.
• Show our approach improves state-of-art and other algorithms on various data.
摘要
Highlights•We solve a primal embedded feature selection problem for nonlinear SVMs.•Use smooth hinge loss and trust region algorithm for bound constrained minimization.•Propose alternating optimization approach to break problem into two smaller ones.•Propose explicit margin maximization to solve feature selection subproblem.•Show our approach improves state-of-art and other algorithms on various data.
论文关键词：Feature selection,Nonlinear,Embedded,Support vector machine,Non-convex optimization,Trust-region method,Alternating optimization
论文评审过程：Received 23 April 2013, Revised 11 August 2013, Accepted 1 January 2014, Available online 15 January 2014.
论文官网地址：https://doi.org/10.1016/j.patcog.2014.01.003
