Skip to main content

ICA by Maximizing Non-stability

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 5441)


We propose a new approach for ICA by maximizing the non-stability contrast function in this paper. This new version of ICA is motivated by the Generalized Central Limit Theorem (GCLT), an important extension of classical CLT. We demonstrate that the classical ICA based on maximization of non-Gaussianity is a special case of the new approach of ICA we introduce here which is based on maximization of non-Stability with certain constraints. To be able to quantify non-stability, we introduce a new measure of stability namely Alpha-stable negentropy. A numerical gradient ascent algorithm for the maximization of the alpha-stable negentropy with the objective of source separation is also introduced in this paper. Experiments show that ICA by maximum of non-stability performs very successfully in impulsive source separation problems.


  • ICA
  • non-stability
  • alpha-stable negentropy
  • source separation
  • impulsive signals

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-00599-2_23
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   139.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-00599-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   179.00
Price excludes VAT (USA)


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Kuruoglu, E.E.: Density Parameter Estimation of Skewed alpha-Stable Distribution. IEEE transactions on signal processing 49(10), 2192–2201 (2001)

    MathSciNet  CrossRef  Google Scholar 

  2. Nikias, C.L., Shao, M.: Signal Processing with Alpha-Stable Distributions and Applications. Wiley, New York (1995)

    Google Scholar 

  3. Samorodnitsky, G., Taqqu, M.: Stable non-Gaussian random processes. Chapman and Hall, New York (1994)

    MATH  Google Scholar 

  4. Hyvärinen, A., Oja, E.: Independent Component Analysis: Algorithms and Applications. Neural Networks 13, 411–430 (2004)

    CrossRef  Google Scholar 

  5. Nolan, J.P.: Numerical Calculation of Stable Densities and Distribution Functions. Stochastic Models 13(4), 759–774 (1997)

    MathSciNet  CrossRef  MATH  Google Scholar 

  6. Hyvärinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)

    CrossRef  Google Scholar 

  7. Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, Chichester (2001)

    CrossRef  Google Scholar 

  8. Comon, P.: Independent Component Analysis, A New Concept? Signal Processing 36, 287–314 (1994)

    CrossRef  MATH  Google Scholar 

  9. Hyvärinen, A.: Survey on Independent Component Analysis. Neural Computing Surveys 2, 94–128 (1999)

    Google Scholar 

  10. Sahmoudi, M., Abed-Meraim, K., Benidir, M.: Blind Separation of Impulsive Alpha-Stable Sources Using Minimum Dispersion Criterion. IEEE Signal Processing Letters 12(4), 281–284 (2005)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wang, B., Kuruoglu, E.E., Zhang, J. (2009). ICA by Maximizing Non-stability. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00598-5

  • Online ISBN: 978-3-642-00599-2

  • eBook Packages: Computer ScienceComputer Science (R0)