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  Kernel principal component analysis

Schölkopf, B., Smola, A., & Müller, K.-R. (1997). Kernel principal component analysis. 7th International Conference on Artificial Neural Networks, ICANN 97, Lausanne, Switzerland, 583-588.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E9CE-F Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E9CF-D
Genre: Conference Paper

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 Creators:
Schölkopf, B1, Author           
Smola, AJ, Author
Müller, K-R, Author
Gerstner, Editor
W., Editor
Germond, A., Editor
Hasler, M., Editor
Nicoud, J.-D., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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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
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 3-540-63631-5
URI: http://www.springerlink.com/content/w0t1756772h41872/fulltext.pdf
DOI: 10.1007/BFb0020217
BibTex Citekey: 421
 Degree: -

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Title: 7th International Conference on Artificial Neural Networks
Place of Event: Lausanne, Switzerland
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Title: 7th International Conference on Artificial Neural Networks, ICANN 97, Lausanne, Switzerland
Source Genre: Journal
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Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 583 - 588 Identifier: -