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  Kernel PCA pattern reconstruction via approximate pre-images.

Schölkopf, B., Mika, S., Smola, A., Rätsch, G., & Müller, K.-R. (1998). Kernel PCA pattern reconstruction via approximate pre-images. In L. Niklasson, M. Bodén, & T. Ziemke (Eds.), ICANN 98: 8th International Conference on Artificial Neural Networks, Skövde, Sweden, 2–4 September 1998 (pp. 147-152). London, UK: Springer.

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Schölkopf, B1, Author              
Mika, S, Author
Smola, AJ, Author              
Rätsch, G, Author              
Müller, K-R, Author              
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1External Organizations, ou_persistent22              

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 Abstract: Algorithms based on Mercer kernels construct their solutions in terms of expansions in a high-dimensional feature space F. Previous work has shown that all algorithms which can be formulated in terms of dot products in F can be performed using a kernel without explicitly working in F. The list of such algorithms includes support vector machines and nonlinear kernel principal component extraction. So far, however, it did not include the reconstruction of patterns from their largest nonlinear principal components, a technique which is common practice in linear principal component analysis. The present work proposes an idea for approximately performing this task. As an illustrative example, an application to the de-noising of data clusters is presented.

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 Dates: 1998-09
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: BibTex Citekey: 802
DOI: 10.1007/978-1-4471-1599-1_18
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Title: 8th International Conference on Artificial Neural Networks (ICANN 1998)
Place of Event: Skövde, Sweden
Start-/End Date: 1998-09-02 - 1998-09-04

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Title: ICANN 98: 8th International Conference on Artificial Neural Networks, Skövde, Sweden, 2–4 September 1998
Source Genre: Proceedings
 Creator(s):
Niklasson, L, Editor
Bodén, M, Editor
Ziemke, T, Editor
Affiliations:
-
Publ. Info: London, UK : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 147 - 152 Identifier: ISBN: 978-3-540-76263-8

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Title: Perspectives in Neural Computing
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -