Linear discriminant analysis with maximum correntropy criterion

Wei Zhou*, Sei Ichiro Kamata

*Corresponding author for this work

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

4 Citations (Scopus)


Linear Discriminant Analysis (LDA) is a famous supervised feature extraction method for subspace learning in computer vision and pattern recognition. In this paper, a novel method of LDA based on a new Maximum Correntropy Criterion optimization technique is proposed. The conventional LDA, which is based on L2-norm, is sensitivity to the presence of outliers. The proposed method has several advantages: first, it is robust to large outliers. Second, it is invariant to rotations. Third, it can be effectively solved by half-quadratic optimization algorithm. And in each iteration step, the complex optimization problem can be reduced to a quadratic problem that can be efficiently solved by a weighted eigenvalue optimization method. The proposed method is capable of analyzing non-Gaussian noise to reduce the influence of large outliers substantially, resulting in a robust classification. Performance assessment in several datasets shows that the proposed approach is more effectiveness to address outlier issue than traditional ones.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2012 - 11th Asian Conference on Computer Vision, Revised Selected Papers
Number of pages12
EditionPART 1
Publication statusPublished - 2013 Apr 11
Event11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of
Duration: 2012 Nov 52012 Nov 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7724 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th Asian Conference on Computer Vision, ACCV 2012
Country/TerritoryKorea, Republic of

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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