Robust projection twin support vector machine via DC programming

In this paper, we propose a robust projection twin support vector machine (RPTSVM), where a new truncated L-2-norm distance measure is applied to the with-class scatter to boost the robustness of the classifier when encountering a large number of outliers. In order to further improve the robustness of the model, chance constraints are employed to specify the lower bound of the probability that the distance from the projected samples to the projection of the other class centre is at least one. RPTSVM considers a pair of non-convex non-smooth problems with chance constraints. To solve these difficult problems, a newly designed method based on the difference of convex functions (DC) programs approach is presented. Extensive experiments on artificial datasets and benchmark datasets demonstrate the robustness and feasibility of our proposed RPTSVM.