文献类型：期刊文献

英文题名：Laplacian total margin support vector machine based on within-class scatter

作者：Pei, Huimin[1];Chen, Yanyan[2];Wu, Yankun[1];Zhong, Ping[1]

第一作者：Pei, Huimin

通讯作者：Zhong, P[1]

机构：[1]China Agr Univ, Coll Sci, Beijing 100083, Peoples R China;[2]Beijing Union Univ, Coll Appl Sci & Technol, Beijing 102200, Peoples R China

第一机构：China Agr Univ, Coll Sci, Beijing 100083, Peoples R China

通讯机构：[1]corresponding author), China Agr Univ, Coll Sci, Beijing 100083, Peoples R China.

年份：2017

卷号：119

起止页码：152-165

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20170203224809);Scopus(收录号：2-s2.0-85008179444);WOS:【SCI-EXPANDED(收录号:WOS:000393532400013)】；

基金：The work is supported by the National Natural Science Foundation of China (Grant No. 11171346), the Chinese University Scientific Fund (No. 2016LX002) and the "New Start" Academic Research Projects of Beijing Union University (Zk10201513). The authors gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation of this paper.

语种：英文

外文关键词：Support vector machine; Total margin; Within-class scatter; Manifold regularization; Semi-supervised learning

摘要：Insufficient volume of supervised information is a major challenge for supervised learning. An effective method to handle this problem is semi-supervised learning, which can make full use of the geometric information embedded in unlabeled instances. In this paper, we present a novel laplacian total margin support vector machine based on within-class scatter (LapWCS-TSVM) method to deal with the semi supervised binary classification problem. The proposed LapWCS-TSVM incorporates the total margin algorithm and the manifold regularization into WCS-SVM to help improve its performance. With the help of kernel trick, the proposed LapWCS-TSVM can be easily generalized to non-linear separable case and solved by the optimization programming of the traditional support vector machine. Experiments conducted on artificial datasets, UCI datasets and face recognition datasets show the validity of the newly proposed algorithm. (C) 2016 Elsevier B.V. All rights reserved.