Multi-view twin support vector machines

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.