We develop an approach for feature elimination in support vector machines (and empirical risk minimization), based on recursive elimination of features. We present theoretical properties of this method and show that this is uniformly consistent in finding the correct feature space under certain generalized assumptions. We present case studies to show that the assumptions are met in most practical situations and also present simulation studies to demonstrate performance of the proposed approach.
Dasgupta, Sayan; Goldberg, Yair; and Kosorok, Michael R., "Feature Elimination in Support Vector Machines and Empirical Risk Minimization" (January 2015). The University of North Carolina at Chapel Hill Department of Biostatistics Technical Report Series. Working Paper 44.