Two-class pattern discrimination via recursive optimization of Patrick-Fisher distance

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


A method for the linear discrimination of two classes is presented. It searches for the discriminant direction which maximizes the Patrick-Fisher (PF) distance between the projected class-conditional densities. It is a nonparametric method, in the sense that the densities are estimated from the data. Since the PF distance is a highly nonlinear function, we propose a recursive optimization procedure for searching the directions corresponding to several large local maxima of the PF distance. Its novelty lies in the transformation of the data along a found direction into data with deflated maxima of PF distance and iteration to obtain the next direction. A simulation study indicates the potential of the method to find the sequence of directions with significant class separations.

Original languageEnglish
Title of host publicationTrack B
Subtitle of host publicationPattern Recognition and Signal Analysis
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)081867282X, 9780818672828
StatePublished - 1 Jan 1996
Event13th International Conference on Pattern Recognition, ICPR 1996 - Vienna, Austria
Duration: 25 Aug 199629 Aug 1996

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference13th International Conference on Pattern Recognition, ICPR 1996

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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