Fast kernel Fisher discriminant analysis by approximating principle component analysis

Jinghua Wang, Qin Li, Jia You

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

Abstract

The feature extraction efficiency of KFDA (kernel Fisher discriminant analysis) is enslaved to the size of the training sample set. In order for an efficient nonlinear feature extraction, we propose a fast KFDA (FKFDA) in this paper. This FKFDA consists of two parts. First, we select a portion of training samples based on two criteria produced by approximating principle component analysis in the kernel space. Then, referring to the selected training samples as nodes, we formulate FKFDA to improve the efficiency of feature extraction by assuming that nodes can replace all the training samples to express the discriminant vectors. The extraction of a feature from a sample in FKFDA requires only calculates as many kernel functions as the nodes. Moreover, the proposed FKFDA can avoid the small sample size problem. Experimental results on face recognition suggest that the proposed FKFDA can generate well classified features.
Original languageEnglish
Title of host publicationProceedings of the 2011 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2011
Pages630-633
Number of pages4
Volume2
Publication statusPublished - 1 Dec 2011
Event2011 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2011 - Las Vegas, NV, United States
Duration: 18 Jul 201121 Jul 2011

Conference

Conference2011 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2011
CountryUnited States
CityLas Vegas, NV
Period18/07/1121/07/11

Keywords

  • Feature extraction
  • Kernel Fisher discriminant analysis
  • Kernel principal component analysis
  • Pattern classification

ASJC Scopus subject areas

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

Cite this