Least squares recursive projection twin support vector machine for classification

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.

► We propose a least squares projection twin support vector machine (LSPTSVM).
► A regularization term is added in our LSPTSVM.
► The optimization problems of LSPTSVM are positive definite and resulted better generalization ability.
► We just solve two systems of linear equations for LSPTSVM.
► LSPTSVM can easily handle large datasets.