Variable Selection for Kernel Classification
Sarel J. Steel, Nadja Louw, S. Bierman · Communications in Statistics - Simulation and Computation · 2010
In this article, a variable selection procedure, called surrogate selection, is proposed which can be applied when a support vector machine or kernel Fisher discriminant analysis is used in a binary classification problem. Surrogate selection applies the lasso after substituting the kernel discriminant scores for the binary group labels, as well as values for the input variable observations. Empirical results are reported, showing that surrogate selection performs well.