Multi-view Laplacian twin support vector machines
Applied IntelligencePublished 2 September 2014
Xijiong Xie, Shiliang Sun
Citations63
SJR quartileQ2
SJR score0.93
SNIP1.21
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TL;DR
Inspired by the recent success of multi-view learning, this paper proposes multi-View Laplacian twin support vector machines, whose dual optimization problems are quadratic programming problems, and extends them to kernel multi- view LaplACian twin Support vector machines.
Abstract
Twin support vector machines are a recently proposed learning method for pattern classification. They learn two hyperplanes rather than one as in usual support vector machines and often bring performance improvements. Semi-supervised learning has attracted great attention in machine learning in the last decade. Laplacian support vector machines and Laplacian twin support vector machines have been proposed in the semi-supervised learning framework. In this paper, inspired by the recent success of multi-view learning we propose multi-view Laplacian twin support vector machines, whose dual optimization problems are quadratic programming problems. We further extend them to kernel multi-view Laplacian twin support vector machines. Experimental results demonstrate that our proposed methods are effective.
Keywords
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