Least squares recursive projection twin support vector machine for classification

Authors:
Yuan-Hai Shao;Nai-Yang Deng;Zhi-Min Yang
Affiliations:
Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, PR China;College of Science China Agricultural University, Beijing 100083, PR China;Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, PR China
Venue:
Pattern Recognition
Year:
2012

Citing 29
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Abstract

In this paper we formulate a least squares version of the recently proposed projection twin support vector machine (PTSVM) for binary classification. This formulation leads to extremely simple and fast algorithm, called least squares projection twin support vector machine (LSPTSVM) for generating binary classifiers. Different from PTSVM, we add a regularization term, ensuring the optimization problems in our LSPTSVM are positive definite and resulting better generalization ability. Instead of usually solving two dual problems, we solve two modified primal problems by solving two systems of linear equations whereas PTSVM need to solve two quadratic programming problems along with two systems of linear equations. Our experiments on publicly available datasets indicate that our LSPTSVM has comparable classification accuracy to that of PTSVM but with remarkably less computational time.