Nonlinear discriminant analysis

Hongbin Zhang, Eric Clarkson, Harrison H. Barrett

Research output: Contribution to journalArticlepeer-review


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)
Pages (from-to)448-455
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
Issue number1
StatePublished - Jul 3 2001


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

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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