Invariant feature extraction and classification in kernel spaces

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

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

104 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
PublisherNeural information processing systems foundation
Pages526-532
Number of pages7
ISBN (Print)0262194503, 9780262194501
Publication statusPublished - 2000 Jan 1
Externally publishedYes
Event13th Annual Neural Information Processing Systems Conference, NIPS 1999 - Denver, CO, United States
Duration: 1999 Nov 291999 Dec 4

Other

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

Fingerprint

Feature extraction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., & Muller, K. (2000). Invariant feature extraction and classification in kernel spaces. In Advances in Neural Information Processing Systems (pp. 526-532). Neural information processing systems foundation.

Invariant feature extraction and classification in kernel spaces. / Mika, Sebastian; Rätsch, Gunnar; Weston, Jason; Schölkopf, Bernhard; Smola, Alex; Muller, Klaus.

Advances in Neural Information Processing Systems. Neural information processing systems foundation, 2000. p. 526-532.

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

Mika, S, Rätsch, G, Weston, J, Schölkopf, B, Smola, A & Muller, K 2000, Invariant feature extraction and classification in kernel spaces. in Advances in Neural Information Processing Systems. Neural information processing systems foundation, pp. 526-532, 13th Annual Neural Information Processing Systems Conference, NIPS 1999, Denver, CO, United States, 99/11/29.
Mika S, Rätsch G, Weston J, Schölkopf B, Smola A, Muller K. Invariant feature extraction and classification in kernel spaces. In Advances in Neural Information Processing Systems. Neural information processing systems foundation. 2000. p. 526-532
Mika, Sebastian ; Rätsch, Gunnar ; Weston, Jason ; Schölkopf, Bernhard ; Smola, Alex ; Muller, Klaus. / Invariant feature extraction and classification in kernel spaces. Advances in Neural Information Processing Systems. Neural information processing systems foundation, 2000. pp. 526-532
@inproceedings{834db993ec634ec6801502448f6a2b2a,
title = "Invariant feature extraction and classification in kernel spaces",
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.",
author = "Sebastian Mika and Gunnar R{\"a}tsch and Jason Weston and Bernhard Sch{\"o}lkopf and Alex Smola and Klaus Muller",
year = "2000",
month = "1",
day = "1",
language = "English",
isbn = "0262194503",
pages = "526--532",
booktitle = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",

}

TY - GEN

T1 - Invariant feature extraction and classification in kernel spaces

AU - Mika, Sebastian

AU - Rätsch, Gunnar

AU - Weston, Jason

AU - Schölkopf, Bernhard

AU - Smola, Alex

AU - Muller, Klaus

PY - 2000/1/1

Y1 - 2000/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84899018574&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84899018574&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84899018574

SN - 0262194503

SN - 9780262194501

SP - 526

EP - 532

BT - Advances in Neural Information Processing Systems

PB - Neural information processing systems foundation

ER -