Safe sample screening for robust twin support vector machine

Twin support vector machine (TSVM) definitely improves computational speed compared with the classical SVM, and has been widely used in classification and regression problems. However, two problems should be aroused. First, since the convex hinge loss function of TSVM is unbounded, the generalization performance of TSVM declines under the noisy environment. Second, TSVM is challenging to deal with large-scale data. To handle these problem, in this paper, we propose a new method named Safe Sample Screening for robust TSVM (SSS-RTSVM). As the ramp loss is bounded, robust TSVM clips the hinge loss in the traditional soft margin twin support vector machine to the ramp loss, and provides a pair of nonparallel proximal hyperplanes to achieve good anti-noise ability to noisy data and outlier data. However, the non-convex problem of robust TSVM can be considered as a DC programming problem which is computationally inefficient. Then we integrate safe sample screening rules for RTSVM based on the framework of concave-convex procedure (CCCP) to delete the most training samples, i.e., a subset of the samples called support vectors (SVs) is selected to reduce the computational cost without sacrificing the optimal accuracy. Notably, for the proposed SSS-RTSVM, the security guarantee is provided to the sample screening rule. Extensive experiments are conducted on several benchmark datasets to fully demonstrate the robustness and acceleration of the proposed method.

Automated summary

In this paper, a new method named Safe Sample Screening for robust TSVM (SSS-RTSVM) is proposed. SSS-RTSVM clips the hinge loss in the traditional soft margin twin support vector machine to the ramp loss, and provides a pair of nonparallel proximal hyperplanes to achieve good anti-noise ability. Additionally, safe sample screening rules based on CCCP are integrated to reduce the computational cost without sacrificing the optimal accuracy.