文献类型：期刊文献

英文题名：Multiview Learning With Robust Double-Sided Twin SVM

作者：Ye, Qiaolin[1] Huang, Peng[1] Zhang, Zhao[2] Zheng, Yuhui[3] Fu, Liyong[4] Yang, Wankou[5]

第一作者：Ye, Qiaolin

通信作者：Fu, LY[1]

机构：[1]Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Peoples R China;[2]Hefei Univ Technol, Key Lab Knowledge Engn Big Data & Intelligent Int, Minist Educ, Hefei 230009, Peoples R China;[3]Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Jiangsu Engn Ctr Network Monitoring, Nanjing 210044, Peoples R China;[4]Chinese Acad Forestry, Inst Forest Resource Informat Techn, Beijing 100091, Peoples R China;[5]Southeast Univ, Sch Automat, Nanjing 210096, Peoples R China

年份：0

外文期刊名：IEEE TRANSACTIONS ON CYBERNETICS

收录：;EI(收录号：20213910953754);Scopus(收录号：2-s2.0-85115715351);WOS:【SCI-EXPANDED(收录号:WOS:000732306000001)】；

基金：Manuscript received February 18, 2021; accepted May 31, 2021. This work was supported in part by the Central Public-Interest Scientific Institution Basal Research Fund under Grant CAFYBB2017ZY002 and Grant CAFYBB2019QD003; in part by the National Science Foundation of China under Grant 62072246, Grant U20B2065, Grant 62072151, and Grant 61773117; and in part by the Anhui Provincial Natural Science Fund for Distinguished Young Scholars under Grant 2008085J30. This article was recommended by Associate Editor D. Tao. (Qiaolin Ye, Peng Huang, and Zhao Zhang contributed equally to this work.) (Corresponding author: Liyong Fu.)

语种：英文

外文关键词：Support vector machines; Eigenvalues and eigenfunctions; Robustness; Task analysis; Standards; Minimization; Linear programming; Double-sided constraints; multiplane support vector machine (SVM); multiview classification; outlier robustness

摘要：Multiview learning (MVL), which enhances the learners' performance by coordinating complementarity and consistency among different views, has attracted much attention. The multiview generalized eigenvalue proximal support vector machine (MvGSVM) is a recently proposed effective binary classification method, which introduces the concept of MVL into the classical generalized eigenvalue proximal support vector machine (GEPSVM). However, this approach cannot guarantee good classification performance and robustness yet. In this article, we develop multiview robust double-sided twin SVM (MvRDTSVM) with SVM-type problems, which introduces a set of double-sided constraints into the proposed model to promote classification performance. To improve the robustness of MvRDTSVM against outliers, we take L1-norm as the distance metric. Also, a fast version of MvRDTSVM (called MvFRDTSVM) is further presented. The reformulated problems are complex, and solving them are very challenging. As one of the main contributions of this article, we design two effective iterative algorithms to optimize the proposed nonconvex problems and then conduct theoretical analysis on the algorithms. The experimental results verify the effectiveness of our proposed methods.