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Subtitle:
Article type: Research Article
Authors: Xie, Xijiong | Sun, Shiliang*
Affiliations: Department of Computer Science and Technology, East China Normal University, Shanghai, China
Correspondence: [*] Corresponding author: Shiliang Sun, Department of Computer Science and Technology, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China. Tel.: +86 21 54345183; Fax: +86 21 54345119; E-mail:slsun@cs.ecnu.edu.cn
Abstract: Twin support vector machines are a recently proposed learning method for binary classification. They learn two hyperplanes rather than one as in conventional support vector machines and often bring performance improvements. Multi-view learning is concerned about learning from multiple distinct feature sets, which aims to exploit distinct views to improve generalization performance. In this paper, we propose multi-view twin support vector machines by solving a pair of quadratic programming problems. This paper gives a detailed derivation of the Lagrange dual optimization formulation. The linear multi-view twin support vector machines are further generalized to the nonlinear case by the kernel trick. Experimental results demonstrate that our proposed methods are effective.
Keywords: Twin support vector machines, multi-view learning, quadratic programming, lagrange dual optimization
DOI: 10.3233/IDA-150740
Journal: Intelligent Data Analysis, vol. 19, no. 4, pp. 701-712, 2015
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