Skip to main content
SpringerLink
Log in
Book cover

International Conference on Artificial Neural Networks

ICANN 1997: Artificial Neural Networks — ICANN'97 pp 529–534Cite as

Entropy optimization

Application to blind source separation

  • Part IV: Signal Processing: Blind Source Separation, Vector Quantization, and Self-Organization
  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

Abstract

This paper proposes an approach for entropy optimization by neural networks. A brief introduction to this problem is given. A simple neural algorithm based upon MSE minimization is provided. Validation of this algorithm is given by an application to the Source Separation problem.

This is a preview of subscription content, log in via an institution.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. J-F. Cardoso and B. Laheld. Equivariant adaptive source separation. IEEE Trans. on S.P., 44(12):3017–3030, December 1996.

    Google Scholar 

  2. N. Charkani and Y. Deville. Optimization of the asymptotic performance of time-domain convolutive source separation algorithms. In ESANN'97, pages 273–278, Bruges, Belgium, April 1997.

    Google Scholar 

  3. P. Comon. Independent component analysis, a new concept? Signal Processing, 36(3):287–314, April 1994.

    Google Scholar 

  4. C. Fyfe and R. Baddeley. Non-linear data structure extraction using simple hebbian networks. Biological Cybernetics, (72):533–541, 1995.

    Google Scholar 

  5. P.J. Huber. Projection pursuit. The Annals of Statistics, 13(2):435–475, 1985.

    Google Scholar 

  6. C. Jutten and J. Hérault. Blind separation of sources, Part I: An adaptive algorithm based on a neuromimetic architecture. Signal Processing, 24(1):1–10, 1991.

    Google Scholar 

  7. J. Karhunen, E. Oja, L. Wang, R. Vigário, and J. Joutsensalo. A class of neural networks for independant component analysis. IEEE trans. N.N., 8(3):486–504, May 1997.

    Google Scholar 

  8. R. Linsker. Self-organization in a perceptual network. Computer, (21):105–117, 1988.

    Google Scholar 

  9. D. T. Pham, P. Garat, and C. Jutten. Separation of a mixture of independent sources through a maximum likelihood approach. In J. Vandewalle, R. Boite, M. Moonen, and A. Oosterlinck, editors, Signal Processing VI, Theories and Applications, pages 771–774, Brussels, Belgium, August 1992. Elsevier.

    Google Scholar 

  10. N. N. Schraudolph. Optimization of entropy with neural networks. PhD thesis, University of California, San Diego, 1995.

    Google Scholar 

  11. H.H. Yang and S.I. Amari. Adaptive on-line learning algorithms for blind separation-maximum entropy and minimum mutual information. Neural Computation, 1997. Accepted.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LTIRF/INPG, 46 Avenue Félix Viallet, 38000, Grenoble, France

    Anisse Taleb & Christian Jutten

Authors
  1. Anisse Taleb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christian Jutten
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Taleb, A., Jutten, C. (1997). Entropy optimization. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020208

Download citation

  • DOI: https://doi.org/10.1007/BFb0020208

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation