Invariant feature extraction and classification in kernel spaces

Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex Smola, Klaus Robert Müller

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

105 Citations (Scopus)

Abstract

We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinear variant of the Rayleigh coefficient' we propose non-linear generalizations of Fisher's discriminant and oriented PCA using Support Vector kernel functions. Extensive simulations show the utility of our approach.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999
PublisherNeural information processing systems foundation
Pages526-532
Number of pages7
ISBN (Print)0262194503, 9780262194501
Publication statusPublished - 2000
Event13th Annual Neural Information Processing Systems Conference, NIPS 1999 - Denver, CO, United States
Duration: 1999 Nov 291999 Dec 4

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other13th Annual Neural Information Processing Systems Conference, NIPS 1999
CountryUnited States
CityDenver, CO
Period99/11/2999/12/4

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Fingerprint Dive into the research topics of 'Invariant feature extraction and classification in kernel spaces'. Together they form a unique fingerprint.

  • Cite this

    Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., & Müller, K. R. (2000). Invariant feature extraction and classification in kernel spaces. In Advances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999 (pp. 526-532). (Advances in Neural Information Processing Systems). Neural information processing systems foundation.