Nonlinear discriminant analysis

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


We describe a new nonlinear discriminant analysis method for feature extraction. This method applies a nonsingular transform to the data such that the transformed data have a Gaussian distribution. Then a Bayes likelihood ratio is calculated for the transformed data. The nonsingular transform makes use of wavelet transforms and histogram matching. Wavelet transforms are an effective tool in analyzing data structures. Histogram matching is applied to the wavelet coefficients and the ordinary image pixel values in order to create a transformed image that has the desired Gaussian statistics.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsM. Sonka, K.M. Hanson
Number of pages8
Publication statusPublished - 2001
EventMedical Imaging 2001 Image Processing - San Diego, CA, United States
Duration: Feb 19 2001Feb 22 2001


OtherMedical Imaging 2001 Image Processing
CountryUnited States
CitySan Diego, CA



  • Histogram matching
  • Hypothesis testing
  • Nonlinear discriminant analysis
  • Nonsingular transform
  • Wavelets

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Cite this

Zhang, H., Clarkson, E. W., & Barrett, H. H. (2001). Nonlinear discriminant analysis. In M. Sonka, & K. M. Hanson (Eds.), Proceedings of SPIE - The International Society for Optical Engineering (1 ed., Vol. 4322, pp. 448-455)