A new approximation method of the quadratic discriminant function

Shin'ichiro Omachi, Fang Sun, Hirotomo Aso

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

9 Citations (Scopus)

Abstract

For many statistical pattern recognition methods, distributions of sample vectors are assumed to be normal, and the quadratic discriminant function derived from the probability density function of multivariate normal distribution is used for classification. However, the computational cost is O(n2) for n-dimensional vectors. Moreover, if there are not enough training sample patterns, covariance matrix can not be estimated accurately. In the case that the dimensionality is large, these disadvantages markedly reduce classification performance. In order to avoid these problems, in this paper, a new approximation method of the quadratic discriminant function is proposed. This approximation is done by replacing the values of small eigenvalues by a constant which is estimated by the maximum likelihood estimation. This approximation not only reduces the computational cost but also improves the classification accuracy.

Original languageEnglish
Title of host publicationAdvances in Pattern Recognition - Joint IAPR International Workshops, SSPR 2000 and SPR 2000, Proceedings
EditorsFrancesc J. Ferri, Jose M. Inesta, Adnan Amin, Pavel Pudil
PublisherSpringer Verlag
Pages601-610
Number of pages10
ISBN (Print)3540679464, 9783540679462
DOIs
Publication statusPublished - 2000
Event8th Meeting of the International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2000 and 3rd International Workshop on Statistical Techniques in Pattern Recognition, SPR 2000 - Alicante, Spain
Duration: 2000 Aug 302000 Sep 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1876 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th Meeting of the International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2000 and 3rd International Workshop on Statistical Techniques in Pattern Recognition, SPR 2000
CountrySpain
CityAlicante
Period00/8/3000/9/1

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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