Orthogonal projection pursuit using genetic optimization

Jilin Tu, T. Huang, R. Beveridge, M. Kirby

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


Projection pursuit is concerned with finding interesting low-dimensional subspace of multivariate data. In this paper we proposed a genetic optimization approach to find the globally optimal orthogonal subspace given training data and user defined criterion on what subspaces are interesting. We then applied this approach to human face recognition. Suppose face recognition is done by simple correlation, a subspace is obtained using our approach that achieve the lowest error rate of face recognition given FERET data set as training set. As Yambor [W.S. Yambor, et al., 2000] showed in experiments that PCA subspace is a pretty good subspace for correlation-based face recognition, we compared the performance of the sub-space we obtained with that of PCA subspace. Experiment result showed this subspace outperformed PCA subspace.

Original languageEnglish (US)
Title of host publicationProceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)0780379977
StatePublished - 2003
Externally publishedYes
EventIEEE Workshop on Statistical Signal Processing, SSP 2003 - St. Louis, United States
Duration: Sep 28 2003Oct 1 2003

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings


OtherIEEE Workshop on Statistical Signal Processing, SSP 2003
Country/TerritoryUnited States
CitySt. Louis


  • Constraint optimization
  • Data analysis
  • Error analysis
  • Face recognition
  • Gaussian processes
  • Genetic algorithms
  • Humans
  • Independent component analysis
  • Principal component analysis
  • Training data

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications


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