Compute orthogonal discriminant vectors efficiently for dimension reduction

Jinghua Wang, Qin Li, Jia You

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Fisher discriminant analysis is a popular technique for dimension reduction and feature extraction. However, the discriminant vectors in the naïve Fisher discriminant analysis are often correlated with each other. We propose an efficient method for computing orthogonal discriminant analysis vectors for dimension reduction in Fisher discriminant analysis. In the proposed method, while the first discriminant vector is worked out in the sample space, the others are worked out by maximizing a Fisher criterion defined in a transformed space which is the null space of the previously obtained discriminant vectors. We also propose two algorithms to implement the model. The experimental results show that the proposed method is effective and efficient.
Original languageEnglish
Title of host publicationProceedings of the 2010 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2010
Pages760-764
Number of pages5
Volume2
Publication statusPublished - 1 Dec 2010
Event2010 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2010 - Las Vegas, NV, United States
Duration: 12 Jul 201015 Jul 2010

Conference

Conference2010 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2010
Country/TerritoryUnited States
CityLas Vegas, NV
Period12/07/1015/07/10

Keywords

  • Dimension reduction
  • Fisher discriminant analysis
  • Orthogonal discriminant vectors
  • Pattern recognition

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

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