PLS dimension reduction for classification of microarray data

PLS dimension reduction is known to give good prediction accuracy
in the context of classification with high-dimensional microarray
data. In this paper, PLS is compared with some of the best
state-of-the-art classification methods. In addition, a simple
procedure to choose the number of components is suggested. The
connection between PLS dimension reduction and gene selection is
examined and a property of the first PLS component for binary
classification is proven. PLS can also be used as a visualization
tool for high-dimensional data in the classification framework. The
whole study is based on 9 real microarray cancer data sets.