Scale adjustments for classifiers in high-dimensional, low sample size settings

Abstract

Distance-based classifiers are generally considered to be effective at discriminating between populations that differ in location. Indeed, nearest-neighbour methods and the support vector machine are frequently used in very high-dimensional problems involving gene expression data, where it is believed that elevated levels of expression convey much of the information for classification. However, one problem inherent to distance-based classifiers is that scale differences can mask location differences. In consequence, such classifiers can have poor performance if the information for classification accumulates through a large number of relatively small location differences in data components, r..