Feature generation by simple FLD

Minora Fukumi, Yasue Mitsukura

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


This paper presents a new algorithm for feature generation, which is approximately derived based on geometrical interpretation of the Fisher linear discriminant analysis. In a field of pattern recognition or signal processing, the principal component analysis (PCA) is often used for data compression and feature extraction. Furthermore, iterative learning algorithms for obtaining eigen-vectors have been presented in pattern recognition and image analysis. Their effectiveness has been demonstrated on computational time and pattern recognition accuracy in many applications. However, recently the Fisher linear discriminant (FLD) analysis has been used in such a field, especially face image analysis. The drawback of FLD is a long computational time in compression of large-sized between-class and within-class covariance matrices. Usually FLD has to carry out minimization of a within-class variance. However in this case the inverse matrix of the within-class covariance matrix cannot be obtained, since data dimension is higher than the number of data and then it includes many zero eigenvalues. In order to overcome this difficulty, a new iterative feature generation method, a simple FLD is introduced and its effectiveness is demonstrated.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
PublisherSpringer Verlag
Number of pages7
ISBN (Print)3540288945, 9783540288947
Publication statusPublished - 2005 Jan 1
Externally publishedYes
Event9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia
Duration: 2005 Sept 142005 Sept 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3681 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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