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Blind Source Separation with Pattern Expression NMF

  • Junying Zhang
  • Zhang Hongyi
  • Le Wei
  • Yue Joseph Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

Independent component analysis (ICA) is a widely applicable and effective approach in blind source separation (BSS) for basic ICA model, but with limitations that sources should be statistically independent, while more common situation is BSS for non-negative linear (NNL) model where observations are linear combinations of non-negative sources with non-negative coefficients and sources may be statistically dependent. By recognizing the fact that BSS for basic ICA model corresponds to matrix factorization problem, in this paper, a novel idea of BSS for NNL model is proposed that the BSS for NNL corresponds to a non-negative matrix factorization problem and the non-negative matrix factorization (NMF) technique is utilized. For better expression of the patterns of the sources, the NMF is further extended to pattern expression NMF (PE-NMF) and its algorithm is presented. Finally, the experimental results are presented which show the effectiveness and efficiency of the PE-NMF to BSS for a variety of applications which follow NNL model.

Keywords

Independent Component Analysis Independent Component Analysis Blind Source Separation Dependent Source Mixed Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley, New York (2001)CrossRefGoogle Scholar
  2. 2.
    Hoyer, P.O., Hyvärinen, A.: Independent Component Analysis Applied to Feature Extrac-tion from Colour and Stereo Images. Network: Computation in Neural Systems 11(3), 191–210 (2000)MATHCrossRefGoogle Scholar
  3. 3.
    Haykin, S.: Neural networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall. Inc., Englewood Cliffs (1999)MATHGoogle Scholar
  4. 4.
    Zhang, J.Y., Wei, L., Wang, Y.: Computational Decomposition of Molecular Signatures based on Blind Source Separation of Non-negative Dependent Sources with NMF. In: 2003 IEEE International Workshop on Neural Networks for Signal Processing, Toulouse, France, September 17-19 (2003)Google Scholar
  5. 5.
    Guillamet, D., Vitria, J.: Classifying Faces with Non-negative Matrix Factorization. In: Escrig, M.T., Toledo, F.J., Golobardes, E. (eds.) CCIA 2002. LNCS (LNAI), vol. 2504, pp. 24–31. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Guillamet, D., Vitria, J.: Application of Non-negative Matrix Factorization to Dynamic Positron Emission Tomography. In: Proceedings of the International Conference on Inde-pendent Component Analysis and Signal Separation (ICA 2001), San Diego, California, December 9-13, pp. 629–632 (2001)Google Scholar
  7. 7.
    Guillamet, D., Vitria, J.: Unsupervised Learning of Part-based Representations. In: Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns, September 5-7, pp. 700–708 (2001)Google Scholar
  8. 8.
    Hesse, C.W., James, C.J.: The FastICA Algorithm with Spatial Con-straints. IEEE Signal Processing Letters 12(11), 792–795 (2005)CrossRefGoogle Scholar
  9. 9.
    Lee, D., Seung, H.S.: Learning the Parts of Objects by Non-negative Matrix Factorization. Nature 401, 788–791 (1999)CrossRefGoogle Scholar
  10. 10.
    Lee, D., Seung, H.S.: Algorithms for Non-negative Matrix Factorization. Advances in Neural Information Processing Systems 13, 556–562 (2001)Google Scholar
  11. 11.
    Novak, M., Mammone, R.: Use of Non-negative Matrix Factorization for Language Model Adaptation in A Lecture Transcription Task. In: Proceedings of the 2001 IEEE Conference on Acoustics, Speech and Signal Processing, Salt Lake City, UT, May 2001, vol. 1, pp. 541–544 (2001)Google Scholar
  12. 12.
    Guillamet, D., Bressan, M., Vitria, J.: Weighted Non-negative Matrix Factorization for Local Representations. In: Proc. of Computer Vision and Pattern Recognition (2001)Google Scholar
  13. 13.
    Foldiak, P.: Forming Sparse Representations by Local Anti-Hebbian Learning. Biological Cybernetics 64(2), 165–170 (1990)CrossRefGoogle Scholar
  14. 14.
    Khan, J., et al.: Classification and Diagnostic Prediction of Cancers Using Gene Expression Profiling and Artificial Neural Networks. Nature Medicine 7(6), 673–679 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Junying Zhang
    • 1
    • 2
  • Zhang Hongyi
    • 1
  • Le Wei
    • 1
  • Yue Joseph Wang
    • 3
  1. 1.School of Computer Science and EngineeringXidian UniversityXi’anP.R. China
  2. 2.Research Institute of Electronics EngineeringXidian UniversityXi’anP.R. China
  3. 3.Department of Electrical and Computer EngineeringVirginia Polytechnic Institute and State UniversityAlexandriaUSA

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