Gradient Algorithm for Nonnegative Independent Component Analysis

  • Shangming Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


A novel algorithm is proposed for the nonnegative independent component analysis. In the algorithm, we employ the gradient algorithm with some modifications to separate nonnegative independent sources from mixtures. Since the local convergence of the gradient algorithm is already proved, the result in this paper will be considered one of the convergent nonnegative ICA algorithms. Simulation shows the proposed algorithm can separate the mixtures of nonnegative signals very successfully.


Independent Component Analysis Original Signal Independent Component Analysis Gradient Algorithm Permutation Matrix 
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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  1. 1.
    Jutten, C., Herault, J.: Blind Separation of Sources, Part I: An Adaptive Algorithm Based on Neuromimetic Architecture. Signal Processing 24, 1–10 (1991)MATHCrossRefGoogle Scholar
  2. 2.
    Comon, P.: Independent Component Analysis – A New Concept? Signal Processing 36, 287–314 (1994)MATHCrossRefGoogle Scholar
  3. 3.
    Pope, K.J., Bogner, R.E.: Blind Signal Separation I: Linear, Instantaneous Combinations. Digital Signal Process 6, 5–16 (1996)CrossRefGoogle Scholar
  4. 4.
    Pope, K.J., Bogner, R.E.: Blind Signal Separation II: Linear, Convolutive Combinations. Digital Signal Process 6, 17–28 (1996)CrossRefGoogle Scholar
  5. 5.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley Interscience Publication, New York (2001)CrossRefGoogle Scholar
  6. 6.
    Hyvärinen, E.A.: A Fast Fixed-point Algorithm for Independent Component Analysis. Neural Computation 9, 1483–1492 (1997)CrossRefGoogle Scholar
  7. 7.
    Tsuge, S., Shishibori, M., Kuroiwa, S., Kita, K.: Dimensionality Reduction Using Nonnegative Matrix Factorization for Information Retrival. In: Proc. IEEE Int. Conf. System, Man, and Cybernetics, Tucson, AZ, vol. 2, pp. 960–965 (2001)Google Scholar
  8. 8.
    Parra, L., Spence, C., Ziehe, A., Muller, K.R.: Unmixing Hyperspectral Data. In: Proc. Advances in Neural Information Processing System, Denver, CO, vol. 12, pp. 942–948 (2000)Google Scholar
  9. 9.
    Lee, J.S., Lee, D.D., Choi, S., Lee, D.S.: Application of Nonnegative Matrix Factorization to Dynamic Positron Emission Tomography. In: Conf. of Independent Component Analysis and Signal Separation, San Diego, CA, December 2000, pp. 629–632 (2000)Google Scholar
  10. 10.
    Paatero, P., Tapper, U.: Positive Matrix Factorization for Language Model with Optimal Utilization of Error Estimates of Data Values. Environmetr. 5, 111–126 (1994)CrossRefGoogle Scholar
  11. 11.
    Plumbly, M., Oja, E.: A “Nonnegative PCA” Algorithm for Independent Component Analysis. IEEE Transactions on Neural Networks 15 (2004)Google Scholar
  12. 12.
    Hyvärinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks 10 (1999)Google Scholar
  13. 13.
    Plumbley, M.: Condotions for Nonnegative Inpendent Component Analysis. IEEE Signal Processing Letters 9 (2002)Google Scholar
  14. 14.
    Plumbley, M.: Algorithms for Nonnegative Inpendent Component Analysis. IEEE Transactions on Neural Networks 4 (2003)Google Scholar
  15. 15.
    Xu, L.: Least Mean Square Error Reconstruction Principle for Self-organizing Neural-nets. Neural Networks 6, 627–648 (1993)CrossRefGoogle Scholar
  16. 16.
    Hyvärinen, A., Oja, E.: Independent Component Analysis: Algorithms and Applications. Neural Networks 13, 411–430 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shangming Yang
    • 1
  1. 1.Computational Intelligence Laboratory, School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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