J. Japan Statist. Soc., Vol. 37 (No. 1), pp. 123-134, 2007

Comparison of Discrimination Methods for High Dimensional Data

Muni S. Srivastava and Tatsuya Kubokawa

Abstract. In microarray experiments, the dimension p of the data is very large but there are only a few observations N on the subjects/patients. In this article, the problem of classifying a subject into one of two groups, when p is large, is considered. Three procedures based on the Moore-Penrose inverse of the sample covariance matrix, and an empirical Bayes estimate of the precision matrix are proposed and compared with the DLDA procedure.

Key words and phrases: Classification, discrimination analysis, minimum distance, Moore-Penrose inverse.

[Full text] (PDF 140 KB)