Two-group classification with high-dimensional correlated data: A factor model approach

Abstract

A class of linear classification rules, specifically designed for high-dimensional problems, is proposed. The new rules are based on Gaussian factor models and are able to incorporate successfully the information contained in the sample correlations. Asymptotic results, that allow the number of variables to grow faster than the number of observations, demonstrate that the worst possible expected error rate of the proposed rules converges to the error of the optimal Bayes rule when the postulated model is true, and to a slightly larger constant when this model is a reasonable approximation to the data generating process. Numerical comparisons suggest that, when combined with appropriate variable selection strategies, rules derived from one-factor models perform comparably, or better, than the most successful extant alternatives under the conditions they were designed for. The proposed methods are implemented as an R package named HiDimDA, available from the CRAN repository.