Laplacian total margin support vector machine based on within-class scatter
作者：
Highlights：
• A novel classification algorithm named LapWCS-TSVM is proposed.
• To effectively exploit the geometric information from unlabeled instances via the manifold regularization term.
• To capture data structure information by incorporating minimum within-class scatter as in the WCS-SVM.
• To avoid the disadvantage of loss of information contained in the majority of training instances by adopting total margin algorithm to substitute the traditional soft margin algorithm.
• Validity is investigated by comparing it with related classifiers on artificial datasets, UCI datasets and face recognition datasets.
摘要
•A novel classification algorithm named LapWCS-TSVM is proposed.•To effectively exploit the geometric information from unlabeled instances via the manifold regularization term.•To capture data structure information by incorporating minimum within-class scatter as in the WCS-SVM.•To avoid the disadvantage of loss of information contained in the majority of training instances by adopting total margin algorithm to substitute the traditional soft margin algorithm.•Validity is investigated by comparing it with related classifiers on artificial datasets, UCI datasets and face recognition datasets.
论文关键词：Support vector machine,Total margin,Within-class scatter,Manifold regularization,Semi-supervised learning
论文评审过程：Received 6 July 2016, Revised 21 November 2016, Accepted 8 December 2016, Available online 8 December 2016, Version of Record 25 January 2017.
论文官网地址：https://doi.org/10.1016/j.knosys.2016.12.009
