A New Linear Discriminant Analysis Method to Address the Over-Reducing Problem

Huan Wan, Gongde Guo, Hui Wang, Xin Wei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

Linear discriminant analysis (LDA) is an effective and efficient linear dimensionality reduction and feature extraction method. It has been used in a broad range of pattern recognition tasks including face recognition, document recognition and image retrieval. When applied to fewer-class classification tasks (such as binary classification), however, LDA suffers from the over-reducing problem – insufficient number of features are extracted for describing the class boundaries. This is due to the fact that LDA results in a fixed number of reduced features, which is one less the number of classes. As a result, the classification performance will suffer, especially when the classification data space has high dimensionality. To cope with the problem we propose a new LDA variant, orLDA (i.e., LDA for over-reducing problem), which promotes the use of individual data instances instead of summary data alone in generating the transformation matrix. As a result orLDA will obtain a number of features that is independent of the number of classes. Extensive experiments show that orLDA has better performance than the original LDA and two LDA variants – uncorrelated LDA and orthogonal LDA.
Original languageEnglish
Title of host publicationPattern Recognition and Machine Intelligence: 6th International Conference, PReMI 2015, Warsaw, Poland, June 30 - July 3, 2015, Proceedings
Place of PublicationSwitzerland
PublisherSpringer
Pages65-72
Number of pages8
ISBN (Electronic)978-3-319-19941-2
ISBN (Print)978-3-319-19940-5
DOIs
Publication statusPublished - 23 Jun 2015
Externally publishedYes

Publication series

NameLecture Notes in Computer Science
Volume9124

Bibliographical note

The 6th International Conference on Pattern Recognition and Machine Intelligence ; Conference date: 01-01-2015

Keywords

  • Feature extraction
  • Linear Discriminant Analysis
  • face recognition and verification

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