Regularized Discriminant Analysis and Its Application in Microarrays

In this paper, we introduce a modified version of linear discriminant analysis, called “shrunken centroids regularized discriminant analysis” (SCRDA). This method generalizes the idea of “nearest shrunken centroids” (NSC) [Tibshirani et al., 2003] into the classical discriminant analysis. The SCRDA method is specially designed for classification problems in high dimension low sample size situations, for example, microarray data. Through both simulated data and real life data, it is shown that this method performs very well in multivariate classification problems, often outperforms the PAM method and can be as competitive as the SVM classifiers. It is also suitable for feature elimination purpose and can be used as gene selection method. The open source R package for SCRDA is available and will be added to the R libraries in the near future.