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Independent Component Analysis

Chapter

Abstract

Independent Component Analysis(ICA) is one of the methods for solving blind source separation in blind signal processing. This method seeks for a linear coordinate system to produce signals that are mutually statistically independent. Compared with Principal Component Analysis (PCA) based on correlation transform, ICA decorrelates signals and reduces the correlation in higher-order statistics. The existing blind processing algorithms are mainly based on ICA and will be unified to a certain extent through the research on ICA.

Keywords

Probability Density Function Mutual Information Independent Component Analysis Independent Component Analysis Blind Source Separation 
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]
    Comon P (1994) Independent component analysis, a new concept? Signal Processing 36: 287–314zbMATHCrossRefGoogle Scholar
  2. [2]
    Cao X R, Liu R W (1996) General approach to blind source separation. IEEE Transactions on Signal Processing 44(3): 562–571CrossRefGoogle Scholar
  3. [3]
    Cardoso J F (1999) High-order contrast for independent component analysis. Neural Computation 11(1): 157–192MathSciNetCrossRefGoogle Scholar
  4. [4]
    Yang H H, Amari S I, Cichocki A (1997) Adaptive on-line learning algorithms for blind separation—Maximum entropy and minimum mutual information. Neural Computation 7(9): 1457–1482CrossRefGoogle Scholar
  5. [5]
    Lee T W (1998) ICA theory and applications. Kluwer Academic, BostonGoogle Scholar
  6. [6]
    Robert S, Everson R (2001) Independent component analysis: Principles and practice. Cambridge University Press, New YorkGoogle Scholar
  7. [7]
    Hyvarinen A, Oja E (1997) A fast fixed-point algorithm for independent component analysis. Neural Computation 9(7): 1483–1492CrossRefGoogle Scholar
  8. [8]
    Amari S I (1998) Natural gradient works efficiently in learning. Neural Computation 10(2): 251–276MathSciNetCrossRefGoogle Scholar
  9. [9]
    Amari S I (1997) Neural learning in structured parameter spaces—Natural Riemannian gradient. In: Advances in Neural Information Processing, Denver, Colorado, 1997, 9: 127–133.Google Scholar
  10. [10]
    Amari S I, Douglas S C (1998) Why natural gradient? In: Proceedings of ICASSP’98, Seattle, Washington, 1998, 2: 1213–1216Google Scholar
  11. [11]
    Douglas S C, Amari S I (2000) Natural gradient adaptation. In: Haykin S, (ed) Unsupervised adaptive filtering. Wiley, New YorkGoogle Scholar
  12. [12]
    Rabiner L R (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2): 257–286CrossRefGoogle Scholar
  13. [13]
    Girolami M (2000) Advances in independent component analysis. Springer, New YorkzbMATHCrossRefGoogle Scholar

Copyright information

© Shanghai Jiao Tong University Press, Shanghai and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Institute of Vibration Shock & NoiseShanghai Jiao Tong UniversityShanghaiChina

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