Invariant feature extraction and classification in kernel spaces

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

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 proceeding › Conference Paper › peer-review

121 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 language	English
Title of host publication	Advances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999
Publisher	Neural Information Processing Systems Foundation
Pages	526-532
Number of pages	7
ISBN (Print)	0262194503, 9780262194501
Publication status	Published - 2000
Event	13th Annual Neural Information Processing Systems Conference, NIPS 1999 - Denver, CO, United States Duration: 29 Nov 1999 → 4 Dec 1999

Publication series

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

Conference

Conference	13th Annual Neural Information Processing Systems Conference, NIPS 1999
Country/Territory	United States
City	Denver, CO
Period	29/11/99 → 4/12/99

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.

@inproceedings{4b95135e695e474e9845ac1006c3fa03,

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 M{\"u}ller, \{Klaus Robert\}",

year = "2000",

language = "English",

isbn = "0262194503",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural Information Processing Systems Foundation",

pages = "526--532",

booktitle = "Advances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999",

note = "13th Annual Neural Information Processing Systems Conference, NIPS 1999 ; Conference date: 29-11-1999 Through 04-12-1999",

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Mika, S, Rätsch, G, Weston, J, Schölkopf, B, Smola, A & Müller, KR 2000, Invariant feature extraction and classification in kernel spaces. in Advances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999. 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, 29/11/99.

Invariant feature extraction and classification in kernel spaces. / Mika, Sebastian; Rätsch, Gunnar; Weston, Jason et al.
Advances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999. Neural Information Processing Systems Foundation, 2000. p. 526-532 (Advances in Neural Information Processing Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › peer-review

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 - Müller, Klaus Robert

PY - 2000

Y1 - 2000

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 - https://www.scopus.com/pages/publications/84899018574

M3 - Conference Paper

SN - 0262194503

SN - 9780262194501

T3 - Advances in Neural Information Processing Systems

SP - 526

EP - 532

BT - Advances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999

PB - Neural Information Processing Systems Foundation

T2 - 13th Annual Neural Information Processing Systems Conference, NIPS 1999

Y2 - 29 November 1999 through 4 December 1999

ER -

Invariant feature extraction and classification in kernel spaces

Abstract

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Conference

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