Indefinite twin support vector machine with DC functions programming
作者：
Highlights：
• We propose a novel regularized TWSVM called ITWSVM for indefinite kernel.
• We introduce a smooth quadratic hinge loss function and a maximum margin regularization term to ITWSVM.
• We theoretically analyze the nature of ITWSVM and expand ITWSVM to indefinite kernels and multi-class classification.
• We introduce difference of convex functions (DC) to solve the non-convex problem in indefinite kernel settings and further propose ITWSVM-DC.
• Extensive experiments demonstrate that ITWSVM-DC is a robust and prominent algorithm and can perform excellently in different situations.
摘要
•We propose a novel regularized TWSVM called ITWSVM for indefinite kernel.•We introduce a smooth quadratic hinge loss function and a maximum margin regularization term to ITWSVM.•We theoretically analyze the nature of ITWSVM and expand ITWSVM to indefinite kernels and multi-class classification.•We introduce difference of convex functions (DC) to solve the non-convex problem in indefinite kernel settings and further propose ITWSVM-DC.•Extensive experiments demonstrate that ITWSVM-DC is a robust and prominent algorithm and can perform excellently in different situations.
论文关键词：SVM,TWSVM,Indefinite kernel,DC Programming,Structural risk minimization principle
论文评审过程：Received 17 October 2019, Revised 12 June 2021, Accepted 20 July 2021, Available online 21 July 2021, Version of Record 29 July 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108195
