Linear discriminant analysis with maximum correntropy criterion

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルComputer Vision, ACCV 2012 - 11th Asian Conference on Computer Vision, Revised Selected Papers
ページ500-511
ページ数12
PART 1
DOI
出版ステータスPublished - 2013 4 11
イベント11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of
継続期間: 2012 11 52012 11 9

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 1
7724 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference11th Asian Conference on Computer Vision, ACCV 2012
CountryKorea, Republic of
CityDaejeon
Period12/11/512/11/9

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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