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2018 On the dimension effect of regularized linear discriminant analysis
Cheng Wang, Binyan Jiang
Electron. J. Statist. 12(2): 2709-2742 (2018). DOI: 10.1214/18-EJS1469


This paper studies the dimension effect of the linear discriminant analysis (LDA) and the regularized linear discriminant analysis (RLDA) classifiers for large dimensional data where the observation dimension $p$ is of the same order as the sample size $n$. More specifically, built on properties of the Wishart distribution and recent results in random matrix theory, we derive explicit expressions for the asymptotic misclassification errors of LDA and RLDA respectively, from which we gain insights of how dimension affects the performance of classification and in what sense. Motivated by these results, we propose adjusted classifiers by correcting the bias brought by the unequal sample sizes. The bias-corrected LDA and RLDA classifiers are shown to have smaller misclassification rates than LDA and RLDA respectively. Several interesting examples are discussed in detail and the theoretical results on dimension effect are illustrated via extensive simulation studies.


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Cheng Wang. Binyan Jiang. "On the dimension effect of regularized linear discriminant analysis." Electron. J. Statist. 12 (2) 2709 - 2742, 2018.


Received: 1 May 2018; Published: 2018
First available in Project Euclid: 15 September 2018

zbMATH: 06942957
MathSciNet: MR3854905
Digital Object Identifier: 10.1214/18-EJS1469


Vol.12 • No. 2 • 2018
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