文献类型：期刊文献

英文题名：Multi-view learning based on nonparallel support vector machine

作者：Tang, Jingjing[1,2];Li, Dewei[1,2];Tian, Yingjie[2,3,4];Liu, Dalian[5]

第一作者：Tang, Jingjing

通讯作者：Tian, YJ[1];Tian, YJ[2];Tian, YJ[3];Liu, DL[4]

机构：[1]Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China;[2]Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China;[3]Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China;[4]Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China;[5]Beijing Union Univ, Dept Basic Course Teaching, Beijing 100101, Peoples R China

第一机构：Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China

通讯机构：[1]corresponding author), Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China;[2]corresponding author), Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China;[3]corresponding author), Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China;[4]corresponding author), Beijing Union Univ, Dept Basic Course Teaching, Beijing 100101, Peoples R China.|[1141788]北京联合大学基础教学部;[11417]北京联合大学;

年份：2018

卷号：158

起止页码：94-108

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20182705404459);Scopus(收录号：2-s2.0-85049347725);WOS:【SCI-EXPANDED(收录号:WOS:000440529200009)】；

基金：This work has been partially supported by grants from National Natural Science Foundation of China (Nos. 61472390, 71731009, 71331005, and 91546201), and the Beijing Natural Science Foundation (No.1162005), and Premium Funding Project for Academic Human Resources Development in Beijing Union University.

语种：英文

外文关键词：Multi-view learning; Nonparallel support vector machine; Alternating direction method of multipliers

摘要：Multi-view learning (MVL) focuses on the problem of learning from the data represented by multiple distinct feature sets. Various successful SVM-based multi-view learning models have been proposed to improve existing learning tasks performance. Since nonparallel support vector machine (NPSVM) is proposed with several incomparable advantages over the state-of-the-art classifiers, it is potentially beneficial to perform the multi-view classification task using NPSVM. In this paper, we build a new multi-view learning model based on nonparallel support vector machine, termed as MVNPSVM. By combining the large margin mechanism and the consensus principle, MVNPSVM not only inherits the advantages of both NPSVM and multi-view learning, but also brings a new insight of extending NPSVM to the multi-view learning field. To solve MVNPSVM efficiently, we adopt the alternating direction method of multipliers (ADMM) as the solution. We theoretically analyze the performance of MVNPSVM from the viewpoints of the consensus analysis and the comparisons with the other two similar methods SVM-2K and multi-view twin support vector machines. Experimental results on 95 binary data sets confirm the effectiveness of the proposed method.