Multi-view twin support vector machines

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:[email protected]

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