A formulation and comparison of two linear feature selection techniques applicable to statistical classification

摘要

Odell and Decell and Coberly have given necessary and sufficient conditions for calculating the matrix of smallest dimension that will preserve the original Bayes classification regions when all population parameters are known. Tubbs et al. have given a solution to the problem of finding the smallest Bayes-classification-region-preserving dimension whenever the population parameters are unknown. This paper presents another solution to the problem and then compares the two solutions to the classical Wilks' method and two other recent methods using a Monte Carlo simulation. The SVD linear feature selection method proposed by Tubbs et al. performs the best over each of the configurations used in this Monte Carlo simulation study.