COMPARATIVE-ANALYSIS OF STATISTICAL PATTERN-RECOGNITION METHODS IN HIGH-DIMENSIONAL SETTINGS

An extensive simulation study is reported comparing eight statistical classification methods, focusing on problems where the number of observations is less than the number of variables. Using a wide range of artificial and real data sets, two types of classifiers are contrasted; methods that classify using all variables, and methods that first reduce the number of dimensions to two or three. The simulations identified regularized discriminant analysis as the overall clearly most powerful classifier, and show that in most cases, a reduction of the dimensionality to two or three dimensions prior to classification increases the error in allocating test observations.