Local support vector machine based dimension reduction

Abstract

Motivated by several recent work that adopt support vector machines into the sufficient dimension reduction research, we propose a local support vector machine based dimension reduction approach. The proposal deals with continuous and binary responses, linear and nonlinear dimension reduction in a unified framework. The localization can also help relax the stringent probabilistic assumptions required by the global methods. Numerical experiments and a real data application demonstrate the efficacy of the proposed approach.