Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Block-diagonal discriminant analysis and its bias-corrected rules

Language

English

Abstract

High-throughput expression profiling allows simultaneous measure of tens of thousands of genes at once. These data have motivated the development of reliable biomarkers for disease subtypes identification and diagnosis. Many methods have been developed in the literature for analyzing these data, such as diagonal discriminant analysis, support vector machines, and k-nearest neighbor methods. The diagonal discriminant methods have been shown to perform well for high-dimensional data with small sample sizes. Despite its popularity, the independence assumption is unlikely to be true in practice. Recently, a gene module based linear discriminant analysis strategy has been proposed by utilizing the correlation among genes in discriminant analysis. However, the approach can be underpowered when the samples of the two classes are unbalanced. In this paper, we propose to correct the biases in the discriminant scores of blockdiagonal discriminant analysis. In simulation studies, our proposed method outperforms other approaches in various settings. We also illustrate our proposed discriminant analysis method for analyzing microarray data studies. © 2013 Walter de Gruyter GmbH, Berlin/Boston.

Keywords

Bias-correction, Block-diagonal, Classification, High-dimensional data, Linear discriminant analysis

Publication Date

2013

Source Publication Title

Statistical Applications in Genetics and Molecular Biology

Volume

12

Issue

3

Start Page

347

End Page

359

Publisher

De Gruyter

DOI

10.1515/sagmb-2012-0017

Link to Publisher's Edition

http://dx.doi.org/10.1515/sagmb-2012-0017

ISSN (print)

21946302

ISSN (electronic)

15446115

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