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Mixture Matrix Identification of Underdetermined Blind Source Separation Based on Plane Clustering Algorithm

  • Beihai Tan
  • Yuli Fu
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 344)

Abstract

Underdetermined blind source separation and sparse component analysis aim at to recover the unknown source signals under the assumption that the observations are less than the source signals and the source signals can be sparse expressed. Many methods to deal with this problem related to clustering. For underdetermined blind source separation model, this paper gives a new plane clustering algorithm to estimate the mixture matrix based on sparse sources information. Good performance of our method is shown by simulations.

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References

  1. 1.
    Hyvarinen, A., Oja, E.: Independent Component Analysis: Algorithms and Applications. Neural Networks, 13 (2000) 411–430CrossRefGoogle Scholar
  2. 2.
    Xie, S. L., Zhang, J. L.: Blind Separation Algorithm of Minimal Mutual Information Based on Rotating Transform. Acta Electronic Sinica, 30(5) (2002) 628–631Google Scholar
  3. 3.
    Zibulevsky, M., Pearlmutter, B.A.: Blind Source Separation by Sparse Decomposition in a Signal Dictionary. Neural computation, 13(4) (2001) 863–882zbMATHCrossRefGoogle Scholar
  4. 4.
    Xie, S. L., He, Z. S., Gao, Y.: Adaptive Theory of Signal Processing. 1st ed. Chinese Science Press, Beijing (2006) 130–223Google Scholar
  5. 5.
    Li, Y., Cichocki, A., Amari, S.: Analysis of Sparse Representation and Blind Source Separation. Neural Computation 16 (2004) 1193–1234zbMATHCrossRefGoogle Scholar
  6. 6.
    Zhang, J. L., Xie, S. L., He, Z.S.: Separability Theory for Blind Signal Separation. Zidonghua Xuebao/Acta Automatica Sinica, 30(3) (2004) 337–344MathSciNetGoogle Scholar
  7. 7.
    Jutten, C., Herault, J.: Blind Separation of Sources, Part I: An Adaptive Algorithm Based on Neuromimetic. Signal Processing, 24 (1991) 1–10zbMATHCrossRefGoogle Scholar
  8. 8.
    Zhang, J. L., Xie, S. L.: Multi-input Signal-output Neural Network Blind Separation Algorithm Based on Penalty Function. Intelligent and Complex Systems, 2 (2003) 353–362zbMATHGoogle Scholar
  9. 9.
    Li, Y., Wang, J., Zurada, J. M.: Blind Extraction of Singularly Mixed Source Signals. IEEE Trans on Neural Networks, 11 (2000) 1413–1422CrossRefGoogle Scholar
  10. 10.
    Li, Y., Wang, J.: Sequential Blind Extraction of Instantaneously Mixed Sources. IEEE Trans. Signal Processing, 50(5) (2002) 997–1006CrossRefGoogle Scholar
  11. 11.
    Belouchrani, A., Cardoso, J. F.: Maximum Likelihood Source Separation for Discrete Sources. In Proc. EUSIPCO, Edinburgh, Scotland (1994) 768–771Google Scholar
  12. 12.
    Lee, T. W., Lewicki, M.S., Girolami, M., Sejnowski, T. J.: Blind Source Separation of More Sources Than Mixtures Using Overcomplete Representation. IEEE Signal Processing Letter, 6 (1999) 87–90CrossRefGoogle Scholar
  13. 13.
    Lewicki, M. S., Sejnowski, T. J.: Learning Overcomplete Representations. Neural computation, 12 (2000) 337–365CrossRefGoogle Scholar
  14. 14.
    Bofill, P., Zibulevsky, M.: Underdetermined Source Separation Using Sparse Representation. Signal processing, 81 (2001) 2353–2362zbMATHCrossRefGoogle Scholar
  15. 15.
    Georiev, P., Theis, F., Cichocki, A.: Sparse Component Analysis and Blind Separation of Underdetermined Mixtures. IEEE Transactions On Neural Networks, 16(4) (2005) 992–996CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Beihai Tan
    • 1
  • Yuli Fu
    • 1
  1. 1.College of Electronic and Communication EngineeringSouth China University of TechnologyChina

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