Fisher discriminant analysis with kernels

Sebastian Mika, Gunnar Ratsch, Jason Weston, Bernhard Scholkopf, Klaus Robert Muller

Research output: Contribution to conferencePaper

2012 Citations (Scopus)

Abstract

A non-linear classification technique based on Fisher's discriminant is proposed. The main ingredient is the kernel trick which allows the efficient computation of Fisher discriminant in feature space. The linear classification in feature space corresponds to a (powerful) non-linear decision function in input space. Large scale simulations demonstrate the competitiveness of our approach.

Original languageEnglish
Pages41-48
Number of pages8
Publication statusPublished - 1999
EventProceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99) - Madison, WI, USA
Duration: 1999 Aug 231999 Aug 25

Other

OtherProceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99)
CityMadison, WI, USA
Period99/8/2399/8/25

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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  • Cite this

    Mika, S., Ratsch, G., Weston, J., Scholkopf, B., & Muller, K. R. (1999). Fisher discriminant analysis with kernels. 41-48. Paper presented at Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99), Madison, WI, USA, .