A safe sample screening rule for Laplacian twin parametric-margin support vector machine

Abstract

Laplacian support vector machine (SVM) for semi-supervised classification has attracted much attention in recent years. As an extension to improve the computational speed, Laplacian twin parametric-margin SVM (LTPSVM) has shown outstanding performance. However, it is still challenging to handle large-scale data. To address this issue, a safe sample screening rule (SSSR) for LTPSVM is proposed in this paper. It could significantly reduce the computational cost. Our proposed SSSR removes most redundant samples, and reduces the scale of optimization problems without sacrificing the optimal accuracy. The most important advantage of SSSR is the safety, i.e., the solutions are exactly the same as the original ones. Numerical experiments on both a synthetical data set and 14 real world data sets have verified the effectiveness of our proposed method.