Independent component analysis

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Independent component analysis
Abbreviations: ICA
Category: Independent component analysis

Multivariate analysis


Functional neuroimaging independent component analysis

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Independent component analysis (ICA) is a group of multivariate analysis techniques. It typically focuses on factorizing a matrix into statistically independent components.


[edit] Methods

  • JADE
  • Infomax, "Bell-Sejnowski"[1]
  • Extended infomax, infomax with flexible sources (sub-Gaussian and super-Gaussian).[2]
  • Probabilistic ICA (PICA) or "noisy ICA'
  • FastICA[3]
  • Mean field ICA
  • Complex ICA
  • Decorrelation
    • Molgedey-Schuster
    • Dynamic component analysis
    • Convolutive ICA
  • Complex ICA
    • Complex fastICA
    • (Wirtinger)[4]

[edit] Tools

[edit] Papers

  1. Comparison of multi-subject ICA methods for analysis of fMRI data
  2. Ingrid Daubechies, E. Roussos, S. Takerkart, M. Benharrosh, C. Golden, K. D'Ardenne, W. Richter, J. D. Cohen, J. Haxby (2009). "Independent component analysis for brain fMRI does not select for independence". Proceedings of the National Academy of Sciences of the United States of America 106(26): 10415-10422.

[edit] References

  1. A. J. Bell, T. J. Sejnowski (1995). "An information maximisation approach to blind separation and blind deconvolution". Neural Computation 7(6): 1129-1159. [1].
  2. T.-W. Lee, M. Girolami, T. J. Sejnowski (1999). "Independent component analysis using an extended infomax algorithm for mixed sub-gaussian and super-gaussian sources". Neural Computation 11(2): 417-441.
  3. A. Hyvärinen (1999). "Fast and robust fixed-point algorithms for independent component analysis". IEEE Transactions on Neural Networks 10(3): 626-634.
  4. T. Adali, Li Hualiang, M. Novey, J.-F. Cardoso (2008). "Complex ICA using nonlinear functions". IEEE Transactions on Signal Processing 56(9): 4536 - 4544. doi: 10.1109/TSP.2008.926104.
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