Clustering by twin support vector machine and least square twin support vector classifier with uniform output coding
作者：
Highlights：
• A large margin TBSVC is proposed by introducing the regularization for clustering.
• The dual problems of TBSVC are positive, and the convergence of the solving algorithm is proved.
• A fast LSTBSVC is proposed with uniform output coding which uniforms its training and output procedure.
• Both TBSVC and LSTBSVC are extended to nonlinear cases by kernel trick.
• Experiments show the better performance of our methods compare with others.
摘要
•A large margin TBSVC is proposed by introducing the regularization for clustering.•The dual problems of TBSVC are positive, and the convergence of the solving algorithm is proved.•A fast LSTBSVC is proposed with uniform output coding which uniforms its training and output procedure.•Both TBSVC and LSTBSVC are extended to nonlinear cases by kernel trick.•Experiments show the better performance of our methods compare with others.
论文关键词：Unsupervised learning,Clustering,Plane-based clustering,Twin support vector clustering,Twin support vector machine
论文评审过程：Received 21 April 2018, Revised 17 July 2018, Accepted 27 August 2018, Available online 30 August 2018, Version of Record 21 November 2018.
论文官网地址：https://doi.org/10.1016/j.knosys.2018.08.034
