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Regularized linear discriminant analysis and its application in microarrays. (English) Zbl 1170.62382

Summary: We introduce a modified version of linear discriminant analysis, called the “shrunken centroids regularized discriminant analysis” (SCRDA). This method generalizes the idea of the “nearest shrunken centroids” (NSC) 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 (using the NSC algorithm) and can be as competitive as the support vector machines classifiers. It is also suitable for feature elimination purpose and can be used as gene selection method. The open source R package for this method (named “rda”) is available on CRAN (http://www.r-project.org) for download and testing.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62H30 Classification and discrimination; cluster analysis (statistical aspects)
92C40 Biochemistry, molecular biology

Software:

rda; R; CRAN
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