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  Kernel principal component analysis

Schölkopf, B., Smola, A., & Müller, K.-R. (1997). Kernel principal component analysis. In W. Gerstner, A. Germond, M. Hasler, & J.-D. Nicoud (Eds.), Artificial Neural Networks - ICANN'97: 7th International Conference Lausanne, Switzerland, October 8–10, 1997 (pp. 583-588). Berlin, Germany: Springer.

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 Creators:
Schölkopf, B1, 2, Author           
Smola, AJ, Author           
Müller, K-R, Author           
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.

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 Dates: 1997-10
 Publication Status: Issued
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 Rev. Type: -
 Identifiers: DOI: 10.1007/BFb0020217
BibTex Citekey: 421
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Title: 7th International Conference on Artificial Neural Networks (ICANN 1997)
Place of Event: Lausanne, Switzerland
Start-/End Date: 1997-10-08 - 1997-10-10

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Title: Artificial Neural Networks - ICANN'97: 7th International Conference Lausanne, Switzerland, October 8–10, 1997
Source Genre: Proceedings
 Creator(s):
Gerstner, W, Editor
Germond, A, Editor
Hasler, M, Editor
Nicoud, J-D, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 583 - 588 Identifier: ISBN: 3-540-63631-5

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 1327 Sequence Number: - Start / End Page: - Identifier: -