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  Kernel feature spaces and nonlinear blind source separation

Harmeling, S., Ziehe, A., Kawanabe, M., & Müller, K.-R. (2002). Kernel feature spaces and nonlinear blind source separation. In T. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14 (pp. 761-768). Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DF16-D Version Permalink: http://hdl.handle.net/21.11116/0000-0005-0EB5-0
Genre: Conference Paper

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 Creators:
Harmeling, S1, Author              
Ziehe, A, Author
Kawanabe, M, Author
Müller, K-R1, Author              
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: In kernel based learning the data is mapped to a kernel feature space of a dimension that corresponds to the number of training data points. In practice, however, the data forms a smaller submanifold in feature space, a fact that has been used e.g. by reduced set techniques for SVMs. We propose a new mathematical construction that permits to adapt to the intrinsic dimension and to find an orthonormal basis of this submanifold. In doing so, computations get much simpler and more important our theoretical framework allows to derive elegant kernelized blind source separation (BSS) algorithms for arbitrary invertible nonlinear mixings. Experiments demonstrate the good performance and high computational efficiency of our kTDSEP algorithm for the problem of nonlinear BSS.

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 Dates: 2002-09
 Publication Status: Published in print
 Pages: -
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Title: Fifteenth Annual Neural Information Processing Systems Conference (NIPS 2001)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2001-12-03 - 2001-12-08

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Title: Advances in Neural Information Processing Systems 14
Source Genre: Proceedings
 Creator(s):
Dietterich, TG, Editor
Becker, S, Editor
Ghahramani, Z, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 761 - 768 Identifier: ISBN: 0-262-27173-7