Kernel uncorrelated and orthogonal discriminant analysis: A unified approach

Tao Xiong, Jieping Ye, Vladimir Cherkassky

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

11 Scopus citations

Abstract

Several kernel algorithms have recently been proposed for nonlinear discriminant analysis. However, these methods mainly address the singularity problem in the high dimensional feature space. Less attention has been focused on the properties of the resulting discriminant vectors and feature vectors in the reduced dimensional space. In this paper, we present a new formulation for kernel discriminant analysis. The proposed formulation includes, as special cases, kernel uncorrelated discriminant analysis (KUDA) and kernel orthogonal discriminant analysis (KODA). The feature vectors of KUDA are uncorrelated, while the discriminant vectors of KODA are orthogonal to each other in the feature space. We present theoretical derivations of proposed KUDA and KODA algorithms. The experimental results show that both KUDA and KODA are very competitive in comparison with other nonlinear discriminant algorithms in terms of classification accuracy.

Original languageEnglish (US)
Title of host publicationProceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Pages125-131
Number of pages7
DOIs
StatePublished - Dec 22 2006
Event2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 - New York, NY, United States
Duration: Jun 17 2006Jun 22 2006

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1
ISSN (Print)1063-6919

Other

Other2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
CountryUnited States
CityNew York, NY
Period6/17/066/22/06

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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