文献类型：期刊文献

英文题名：Coupling privileged kernel method for multi-view learning

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

第一作者：Tang, Jingjing

通讯作者：Tian, YJ[1];Liu, DL[1]

机构：[1]Southwestern Univ Finance & Econ, Sch Business Adm, Chengdu 611130, Sichuan, 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：Southwestern Univ Finance & Econ, Sch Business Adm, Chengdu 611130, Sichuan, Peoples R China

通讯机构：[1]corresponding author), Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China.

年份：2019

卷号：481

起止页码：110-127

外文期刊名：INFORMATION SCIENCES

收录：;EI(收录号：20190106333096);Scopus(收录号：2-s2.0-85059322863);WOS:【SCI-EXPANDED(收录号:WOS:000459846300007)】；

基金：This work has been partially supported by grants from National Natural Science Foundation of China (Nos. 71731009, 61472390, 71331005, 91546201, 71725001, 71471149 and 71271191), the Beijing Natural Science Foundation (No. 1162005), Premium Funding Project for Academic Human Resources Development in Beijing Union University, and Major project of the National Social Science Foundation of China (No. 15ZDB153).

语种：英文

外文关键词：Multi-view learning; Coupling term; Privileged information; Support vector machine; Consensus and complementarity principles

摘要：Multi-view learning concentrates on fully using the data collected from diverse domains or obtained from various feature extractors to learn effectively. The consensus and complementarity principles provide significant guidance in multi-view modeling. Many support vector machine (SVM)-based multi-view learning models have been proposed, which mainly follow the consensus principle through exploiting the label correlation with regularization terms. In this paper, we propose a simple yet effective coupling privileged kernel method for multi-view learning, termed as MCPK. The coupling term included in the primal objective allows the combination of the errors from all views to be minimized, which guarantees the consensus principle. Similar to our previous work PSVM-2V, MCPK realizes the complementarity principle by applying the learning using privileged information (LUPI) paradigm. The proposed model not only fully integrates the information from all views in the learning process, but also maintains the characteristic of different views to some extent. We employ the standard quadratic programming solver to solve MCPK. Further more, we theoretically analyze the performance of MCPK from the viewpoints of the generalization capability and the PSVM-2V and SVM-2K models. Experimental results demonstrate that MCPK compares more favorably than other state-of-the-art multi-view algorithms in terms of classification accuracy and efficiency. (C) 2018 Elsevier Inc. All rights reserved.