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Separation of post-nonlinear mixtures using ACE and temporal decorrelation

2001

Conference Paper

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We propose an efficient method based on the concept of maximal correlation that reduces the post-nonlinear blind source separation problem (PNL BSS) to a linear BSS problem. For this we apply the Alternating Conditional Expectation (ACE) algorithm – a powerful technique from nonparametric statistics – to approximately invert the (post-)nonlinear functions. Interestingly, in the framework of the ACE method convergence can be proven and in the PNL BSS scenario the optimal transformation found by ACE will coincide with the desired inverse functions. After the nonlinearities have been removed by ACE, temporal decorrelation (TD) allows us to recover the source signals. An excellent performance underlines the validity of our approach and demonstrates the ACE-TD method on realistic examples.

Author(s): Ziehe, A. and Kawanabe, M. and Harmeling, S. and Müller, K-R.
Book Title: ICA 2001
Journal: Proceedings of the Third International Workshop on Independent Component Analysis and Blind Signal Separation (ICA 2001)
Pages: 433-438
Year: 2001
Month: December
Day: 0
Editors: Lee, T.-W. , T.P. Jung, S. Makeig, T. J. Sejnowski

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

Event Name: Third International Workshop on Independent Component Analysis and Blind Signal Separation
Event Place: San Diego, CA, USA

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{6365,
  title = {Separation of post-nonlinear mixtures using ACE and temporal decorrelation},
  author = {Ziehe, A. and Kawanabe, M. and Harmeling, S. and M{\"u}ller, K-R.},
  journal = {Proceedings of the Third International Workshop on Independent Component Analysis and Blind Signal Separation (ICA 2001)},
  booktitle = {ICA 2001},
  pages = {433-438},
  editors = {Lee, T.-W. , T.P. Jung, S. Makeig, T. J. Sejnowski},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = dec,
  year = {2001},
  month_numeric = {12}
}