A coordinate descent margin based-twin support vector machine for classification

Twin support vector machines (TWSVMs) obtain faster learning speed by solving a pair of smaller SVM-type problems. In order to increase its efficiency further, this paper presents a coordinate descent margin based twin vector machine (CDMTSVM) compared with the original TWSVM. The major advantages of CDMTSVM lie in two aspects: (1) The primal and dual problems are reformulated and improved by adding a regularization term in the primal problems which implies maximizing the “margin” between the proximal hyperplane and bounding hyperplane, yielding the dual problems to be stable positive definite quadratic programming problems. (2) A novel coordinate descent method is proposed for our dual problems which leads to very fast training. As our coordinate descent method handles one data point at a time, it can process very large datasets that need not reside in memory. Our experiments on publicly available datasets indicate that our CDMTSVM is not only fast, but also shows good generalization performance.