A mathematical programming approach to the kernel fisher algorithm

Sebastian Mika, Gunnar Ratsch, Klaus Robert Müller

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

114 Citations (Scopus)

Abstract

We investigate a new kernel-based classifier: the Kernel Fisher Discriminant (KFD). A mathematical programming formulation based on the observation that KFD maximizes the average margin permits an interesting modification of the original KFD algorithm yielding the sparse KFD. We find that both, KFD and the proposed sparse KFD, can be understood in an unifying probabilistic context. Furthermore, we show connections to Support Vector Machines and Relevance Vector Machines. From this understanding, we are able to outline an interesting kernel-regression technique based upon the KFD algorithm. Simulations support the usefulness of our approach.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000
PublisherNeural information processing systems foundation
ISBN (Print)0262122413, 9780262122412
Publication statusPublished - 2001
Event14th Annual Neural Information Processing Systems Conference, NIPS 2000 - Denver, CO, United States
Duration: 2000 Nov 272000 Dec 2

Publication series

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

Other

Other14th Annual Neural Information Processing Systems Conference, NIPS 2000
CountryUnited States
CityDenver, CO
Period00/11/2700/12/2

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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    Mika, S., Ratsch, G., & Müller, K. R. (2001). A mathematical programming approach to the kernel fisher algorithm. In Advances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000 (Advances in Neural Information Processing Systems). Neural information processing systems foundation.