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  Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces

Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., & Müller, K.-R. (2003). Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5), 623-628. doi:10.1109/TPAMI.2003.1195996.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-6A9C-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-6A9D-4
Genre: Journal Article

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 Creators:
Mika, S, Author
Rätsch, G, Author              
Weston, J1, 2, Author              
Schölkopf, B1, 2, Author              
Smola, A, Author              
Müller, K-R, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh coefficient, we propose nonlinear generalizations of Fisher's discriminant and oriented PCA using support vector kernel functions. Extensive simulations show the utility of our approach.

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 Dates: 2003-04
 Publication Status: Published in print
 Pages: -
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 Rev. Method: -
 Identifiers: DOI: 10.1109/TPAMI.2003.1195996
 Degree: -

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Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  Other : IEEE Trans. Pattern Anal. Mach. Intell.
Source Genre: Journal
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Publ. Info: New York : IEEE Computer Society.
Pages: - Volume / Issue: 25 (5) Sequence Number: - Start / End Page: 623 - 628 Identifier: ISSN: 0162-8828
CoNE: https://pure.mpg.de/cone/journals/resource/954925479551