An Updated Projection Twin Support Vector Machine for Classification

Abstract

Based on projection twin support vector machine (PTSVM) and its extensions, this paper describes an updated PTSVM (UPTSVM) for classification. Compared with existing PTSVMs, UPTSVM has its own advantages. First, similar to the standard support vector machine (SVM), UPTSVM maintains the consistency of the optimization problems in the linear and nonlinear case, which results in the nonlinear formulations can be directly turned into the linear ones. Nevertheless, the existing PTSVMs lose the consistency because of using empirical kernel to construct nonlinear formulations. Second, UPTSVM avoids the inverse of kernel matrixes in the course of solving dual problems, which indicates it can not only reduce computing time but also save storage space. Third, UPTSVM can be practically proved equivalent to the PTSVM with regularization (RPTSVM). Experimental results on lots of data sets show the virtue of the presented method.