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
Link to Publisher's Edition
ISSN (print)
00313203
Recommended Citation
Ching, Wai-Ki, Delin Chu, Li-Zhi Liao, and Xiaoyan Wang. "Regularized orthogonal linear discriminant analysis." Pattern Recognition 45.7 (2012): 2719-2732.