Journal
PATTERN RECOGNITION
Volume 45, Issue 6, Pages 2299-2307Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2011.11.028
Keywords
Pattern classification; Twin support vector machine; Least squares; Projection twin support vector machine
Funding
- National Natural Science Foundation of China [10971223, 10926198, 11071252]
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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. (C) 2011 Elsevier Ltd. All rights reserved.
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