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Kernel feature spaces and nonlinear blind source separation

2002

Conference Paper

ei


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.

Author(s): Harmeling, S. and Ziehe, A. and Kawanabe, M. and Müller, K-R.
Book Title: Advances in Neural Information Processing Systems 14
Journal: Advances in Neural Information Processing Systems 14: Proceedings of the NIPS 2001 Conference
Pages: 761-768
Year: 2002
Month: September
Day: 0
Editors: Dietterich, T. G., S. Becker, Z. Ghahramani
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Fifteenth Annual Neural Information Processing Systems Conference (NIPS 2001)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{6363,
  title = {Kernel feature spaces and nonlinear blind source separation},
  author = {Harmeling, S. and Ziehe, A. and Kawanabe, M. and M{\"u}ller, K-R.},
  journal = {Advances in Neural Information Processing Systems 14: Proceedings of the NIPS 2001 Conference},
  booktitle = {Advances in Neural Information Processing Systems 14},
  pages = {761-768},
  editors = {Dietterich, T. G., S. Becker, Z. Ghahramani},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = sep,
  year = {2002},
  month_numeric = {9}
}