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Estimating Functions for Blind Separation when Sources Have Variance-Dependencies

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Independent Component Analysis and Blind Signal Separation (ICA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3195))

Abstract

The blind separation problem where the sources are not independent, but have variance-dependencies is discussed. Hyvärinen and Hurri[1] proposed an algorithm which requires no assumption on distributions of sources and no parametric model of dependencies between components. In this paper, we extend the semiparametric statistical approach of Amari and Cardoso[2] under variance-dependencies and study estimating functions for blind separation of such dependent sources. In particular, we show that many of ICA algorithms are applicable to the variance-dependent model as well. Our theoretical consequences were confirmed by artificial and realistic examples.

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References

  1. Hyvärinen, A., Hurri, J.: Blind separation of sources that have spatiotemporal variance dependencies. Signal Processing (2004) (to appear)

    Google Scholar 

  2. Amari, S., Cardoso, J.F.: Blind source separation – semiparametric statistical approach. IEEE Trans. on Signal Processing 45, 2692–2700 (1997)

    Article  Google Scholar 

  3. Cardoso, J.F.: Multidimensional independent component analysis. In: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 1998), Seattle, WA (1998)

    Google Scholar 

  4. Hyvärinen, A., Hoyer, P.O., Inki, M.: Topographic independent component analysis. Neural Computation 13 (2001)

    Google Scholar 

  5. Bach, F.R., Jordan, M.I.: Tree-dependent component analysis. In: Uncertainty in Artificial Intelligence: Proceedings of the Eighteenth Conference, UAI 2002 (2002)

    Google Scholar 

  6. Valpola, H., Harva, M., Karhunen, J.: Hierachical models of variance sources. In: Proc. of ICA 2003, Nara, Japan (2003)

    Google Scholar 

  7. Bickel, P., Klaassen, C., Ritov, Y., Wellner, J.: Efficient and Adaptive Estimation for Semiparamtric Models. John Hopkins Univ. Press, Baltimore (1993)

    MATH  Google Scholar 

  8. Amari, S., Kawanabe, M.: Information geometry of estimating functions in semiparametric statistical models. Bernoulli 3, 29–54 (1997)

    Article  MathSciNet  Google Scholar 

  9. Godambe, V. (ed.): Estimating Functions. Oxford Univ. Press, New York (1991)

    MATH  Google Scholar 

  10. Cardoso, J.F.: Estimating equations for source separation. In: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 1997), Munich, Germany, vol. 5, pp. 3449–3452 (1997)

    Google Scholar 

  11. Ziehe, A., Müller, K.R.: TDSEP – an efficient algorithm for blind separation using time structure. In: Niklasson, L., Bodén, M., Ziemke, T. (eds.) Proc. of the 8th Int. Conf. on Artificial Neural Networks (ICANN 1998), Berlin, pp. 675–680. Springer, Heidelberg (1998)

    Google Scholar 

  12. Belouchrani, K., Abed Meraim, J.F.C., Moulines, E.: A blind source separation technique based on second order statistics. IEEE Trans. on Signal Processing, 1009–1020 (1996)

    Google Scholar 

  13. Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Computation 9, 1483–1492 (1997)

    Article  Google Scholar 

  14. Cardoso, J.F., Souloumiac, A.: Blind beamforming for non gaussian signals. IEE Proceedings- F, 362–370 (1993)

    Google Scholar 

  15. Pham, D.T., Cardoso, J.F.: Blind separation of instantaneous mixtures of non-stationary sources. In: Proc. of ICA 2000, Helsinki, Finland, pp. 187–193 (2000)

    Google Scholar 

  16. Amari, S., Cichocki, A., Yang, H.: A new learning algorithm for blind source separation. In: Advances in Neural Information Processing Systems 8, pp. 757–763. MIT Press, Cambridge (1996)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Fraunhofer FIRST.IDA, Kekuléstr. 7, 12489, Berlin, Germany

    Motoaki Kawanabe & Klaus-Robert Müller

  2. University of Potsdam, August-Bebel-Strasse 89, 14482, Potsdam, Germany

    Klaus-Robert Müller

Authors
  1. Motoaki Kawanabe
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  2. Klaus-Robert Müller
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Editor information

Editors and Affiliations

  1. Dept. of Architecture and Computer Technology, University of Granada, Spain

    Carlos G. Puntonet  & Alberto Prieto  & 

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© 2004 Springer-Verlag Berlin Heidelberg

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Kawanabe, M., Müller, KR. (2004). Estimating Functions for Blind Separation when Sources Have Variance-Dependencies. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_18

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  • DOI: https://doi.org/10.1007/978-3-540-30110-3_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23056-4

  • Online ISBN: 978-3-540-30110-3

  • eBook Packages: Springer Book Archive

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