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
Recommended Citation
Pang, Herbert, Tiejun Tong, and Michael Ng. "Block-diagonal discriminant analysis and its bias-corrected rules." Statistical Applications in Genetics and Molecular Biology 12.3 (2013): 347-359.