Document Type
Journal Article
Department/Unit
Department of Mathematics
Title
Covariance-enhanced discriminant analysis
Language
English
Abstract
© 2014 Biometrika Trust. Linear discriminant analysis has been widely used to characterize or separate multiple classes via linear combinations of features. However, the high dimensionality of features from modern biological experiments defies traditional discriminant analysis techniques. Possible interfeature correlations present additional challenges and are often underused in modelling. In this paper, by incorporating possible interfeature correlations, we propose a covariance-enhanced discriminant analysis method that simultaneously and consistently selects informative features and identifies the corresponding discriminable classes. Under mild regularity conditions, we show that the method can achieve consistent parameter estimation and model selection, and can attain an asymptotically optimal misclassification rate. Extensive simulations have verified the utility of the method, which we apply to a renal transplantation trial.
Keywords
Correlation, Graphical lasso, Linear discriminant analysis, Pairwise fusion, Variable selection
Publication Date
2015
Source Publication Title
Biometrika
Volume
102
Issue
1
Start Page
33
End Page
45
Publisher
Oxford University Press
DOI
10.1093/biomet/asu049
Link to Publisher's Edition
http://dx.doi.org/10.1093/biomet/asu049
ISSN (print)
00063444
ISSN (electronic)
14643510
APA Citation
Xu, P., Zhu, J., Zhu, L., & Li, Y. (2015). Covariance-enhanced discriminant analysis. Biometrika, 102 (1), 33-45. https://doi.org/10.1093/biomet/asu049