Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Regularized orthogonal linear discriminant analysis

Language

English

Abstract

In this paper the regularized orthogonal linear discriminant analysis (ROLDA) is studied. The major issue of the regularized linear discriminant analysis is to choose an appropriate regularization parameter. In existing regularized linear discriminant analysis methods, they all select the best regularization parameter from a given parameter candidate set by using cross-validation for classification. An obvious limitation of such regularized linear discriminant analysis methods is that it is not clear how to choose an appropriate candidate set. Therefore, up to now, there is no concrete mathematical theory available in selecting an appropriate regularization parameter in practical applications of the regularized linear discriminant analysis. The present work is to fill this gap. Here we derive the mathematical relationship between orthogonal linear discriminant analysis and the regularized orthogonal linear discriminant analysis first, and then by means of this relationship we find a mathematical criterion for selecting the regularization parameter in ROLDA and consequently we develop a new regularized orthogonal linear discriminant analysis method, in which no candidate set of regularization parameter is needed. The effectiveness of our proposed regularized orthogonal linear discriminant analysis is illustrated by some real-world data sets. © 2012 Elsevier Ltd. All rights reserved.

Keywords

Data dimensionality reduction, Orthogonal linear discriminant analysis, QR factorization, Regularized orthogonal linear discriminant analysis

Publication Date

2012

Source Publication Title

Pattern Recognition

Volume

45

Issue

7

Start Page

2719

End Page

2732

Publisher

Elsevier

DOI

10.1016/j.patcog.2012.01.007

ISSN (print)

00313203

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