Regularized orthogonal linear discriminant analysis

Wai Ki Ching, Delin Chu*, Lizhi LIAO, Xiaoyan Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2719-2732
Number of pages14
JournalPattern Recognition
Volume45
Issue number7
DOIs
Publication statusPublished - Jul 2012

Scopus Subject Areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

User-Defined Keywords

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

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