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

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