Improved twin support vector machine using total margin and graph embedding

Twin support vector machine (TSVM) was proposed recently as a novel binary classifier which aims to seek a pair of nonparallel planes such that each one is closest to the samples of its own class and is at least one distance far from the samples of the other class. In this paper, we improve TSVM and propose a novel graph embedded total margin twin support vector machine (GTM-TSVM). The central idea of GTM-TSVM is the plane of one class is required to be far away from overall samples of the other class. Moreover, the intra-class and inter-class graphs which respectively characterize the proximity relationships between samples of within and between classes are embedded into GTM-TSVM formulation so as to exploit the underlying manifold structure of data. The nonlinear classification with kernels is also studied. The experimental results on several publicly available benchmark data sets confirm the feasibility and effectiveness of the proposed method.