Skip to main content
SpringerLink
Log in

A hierarchical learning rule for independent component analysis

  • Poster Presentations 1
  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

Included in the following conference series:

Abstract

In this paper, a two-layer neural network is presented that organizes itself to perform Independent Component Analysis (ICA). A hierarchical, nonlinear learning rule is proposed which allows to extract the unknown independent source signals out of a linear mixture. The convergence behaviour of the network is analyzed mathematically.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. S. Amari, A. Cichocki, H. H. Yang. Recurrent Neural Networks For Blind Separation of Sources. In Proceedings of the 1995 International Symposium on Nonlinear Theory and Applications, Vol. I, pp. 37–42, 1995.

    Google Scholar 

  2. S. Amari, A. Cichocki, H. H. Yang. A New Learning Algorithm for Blind Signal Separation. In Advances in Neural Information Processing Systems 1995, Vol. 8, MIT Press, 1996.

    Google Scholar 

  3. G. Burel. Blind Separation of Sources: A Nonlinear Algorithm. In Neural Networks, Vol. 5, pp. 937–947, 1992

    Google Scholar 

  4. P. Comon. Independent Component Analysis: A New Concept. In Signal Processing, Vol. 36, pp. 287–314, 1994.

    Google Scholar 

  5. P. Comon, C. Jutten, J.Herault. Blind Separation of Sources, Part II: Problem Statements. In Signal Processing, Vol. 24, pp. 11–20, 1991.

    Google Scholar 

  6. N. Delfosse, P. Loubaton. Adaptive Blind Separation of Independent Sources: A Deflation Approach. In Signal Processing, Vol. 45, pp. 59–83, 1995.

    Google Scholar 

  7. Jolliffe, I.T. Principal Component Analysis. Springer-Verlag, New York, 1986.

    Google Scholar 

  8. C. Jutten, J.Herault. Blind Separation of Sources, Part I: An Adaptive Algorithm Based on a Neuromimetic Architecture. In Signal Processing, Vol. 24, pp. 1–10, 1991.

    Google Scholar 

  9. M. Kendall, A. Stuart. In The Advanced Theory of Statistics, Vol. 1, 1977.

    Google Scholar 

  10. J. Karhunen, E. Oja, L. Wang, R. Vigario, J. Joutsensalo. A Class of Neural Networks for Independent Component Analysis. Technical Report A 28, Helsinki University, Finland, 1995.

    Google Scholar 

  11. Sanger, T.D. Optimal Unsupervised Learning in a Single-Layer Linear Feedforward Neural Network. Neural Networks, 2:459–473, 1989.

    Google Scholar 

  12. E. Sorouchyari. Blind Separation of Sources, Part III: Stability Analysis. In Signal Processing, Vol. 24, pp. 21–29, 1991.

    Google Scholar 

  13. D. L. Wallace. Assymptotic Approximations to Distributions. In Annual Mathematical Statistics, Vol. 29, pp. 635–654, 1958.

    Google Scholar 

  14. L. Wang, J. Karhunen, E. Oja. A Bigradient Optimization Approach for Robust PCA, MCA and Source Separation. In Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, Australia, pp. 1684–1689, 1995.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering & Computer Science, University of Siegen, Hölderlinstr. 3, D-57068, Siegen, Germany

    Bernd Freisleben

  2. Department of Computer Science, University of Darmstadt, Julius-Reiber-Str. 17, D-64293, Darmstadt, Germany

    Claudia Hagen

Authors
  1. Bernd Freisleben
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Claudia Hagen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Freisleben, B., Hagen, C. (1996). A hierarchical learning rule for independent component analysis. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_90

Download citation

  • DOI: https://doi.org/10.1007/3-540-61510-5_90

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation