Discriminant analysis of high-dimensional data: a comparison of principal components analysis and partial least squares data reduction methods

Abstract

Partial least squares (PLS) methods are presented as valuable alternatives to principal components analysis (PCA) for compressing high-dimensional data before performing linear discriminant analysis (LDA). It is shown that using PLS, considerable improvement in class separation and thus discriminant ability can be obtained. In general, fewer of the compressed dimensions are required to give the same level of prediction successes, and for some data sets, PLS methods yield higher prediction success rates than those obtainable using PCA scores. Results are presented for two experimental data sets, comprising mid-infrared spectra of edible oils and plant seeds. The potential dangers of PLS methods are also demonstrated, in particular its ability to introduce apparent groupings into data where there is no inherent class structure.