A Cost Based Reweighted Scheme of Principal Support Vector Machine

Abstract

Principal Support Vector Machine (PSVM) is a recently proposed method that uses Support Vector Machines to achieve linear and nonlinear sufficient dimension reduction under a unified framework. In this work, a reweighted scheme is used to improve the performance of the algorithm. We present basic theoretical results and demonstrate the effectiveness of the reweighted algorithm through simulations and real data application.