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  Kernel PCA and De-noising in feature spaces

Mika, S., Schölkopf, B., Smola, A., Müller, K., Scholz, M., & Rätsch, G. (1999). Kernel PCA and De-noising in feature spaces. Advances in Neural Information Processing Systems 11, 536-542.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E699-C Version Permalink: https://hdl.handle.net/21.11116/0000-0002-DD8F-5
Genre: Conference Paper

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https://papers.nips.cc/paper/1491-kernel-pca-and-de-noising-in-feature-spaces.pdf (Publisher version)
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 Creators:
Mika, S, Author
Schölkopf, B1, Author           
Smola, AJ1, Author           
Müller, KR1, Author           
Scholz, M, Author
Rätsch, G1, Author           
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classification algorithms. But it can also be considered as a natural generalization of linear principal component analysis.
This gives rise to the question how to use nonlinear features for data compression, reconstruction, and de-noising, applications common in linear PCA. This is a nontrivial task, as the results provided by kernel PCA live in some high dimensional feature space and need not have
pre-images in input space. This work presents ideas for finding approximate pre-images, focusing on Gaussian kernels, and shows experimental results using these pre-images in data reconstruction and de-noising on toy examples as well as on real world data.

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 Dates: 1999-05
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 806
 Degree: -

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Title: Twelfth Annual Conference on Neural Information Processing Systems (NIPS 1998)
Place of Event: Denver, CO, USA
Start-/End Date: 1998-11-30 - 1998-12-05

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Title: Advances in Neural Information Processing Systems 11
Source Genre: Journal
 Creator(s):
Kearns, MJ, Editor
Solla, SA, Editor
Cohn, DA, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 536 - 542 Identifier: ISBN: 0-262-11245-0