A Novel Twin Support Vector Machines with Manifold Regularization

Abstract

Twin support vector machine (TWSVM), as a variant of the generalized eigenvalue proximal support vector machine (GEPSVM), attempts to improve the generalization of GEPSVM, whose solution follows from solving two quadratic programming problems (QPPs), each of which is smaller than in a standard SVM. Unfortunately, TWSVM fails to fully consider the local geometry structure and the local underlying descriminant information inside the samples that may be important for classification performance and only preserves the global data structure. In this paper, a novel TWSVM with manifold regularization is proposed by introducing the basic idea of the locality preserving within-class scatter matrix (LPWSM) into TWSVM. We termed this method manifold TWSVM (MTWSVM). MTWSVM not only retains the superior characteristics of TWSVM, but also preserves the local geometry structure between samples and shows the local underlying discriminant information. Experimental results confirm the effectiveness of our method.