Abstract
Independent component analysis (ICA) is a statistical signal processing technique whose main applications are blind source separation, blind deconvolution, and feature extraction. Estimation of ICA is usually performed by optimizing a 'contrast' function based on higher-order cumulants. It is shown how almost any error function can be used to construct a contrast function to perform the ICA estimation. In particular, this means that one can use contrast functions that are robust against outliers. As a practical method for finding the relevant extrema of such contrast functions, a fixed-point iteration scheme is then introduced. The resulting algorithms are quite simple and converge fast and reliably. These algorithms also enable estimation of the independent components one-by-one, using a simple deflation scheme.
Keywords
Independent component analysisBlind signal separationAlgorithmMaxima and minimaDeconvolutionHigher-order statisticsComputer scienceOutlierContrast (vision)Blind deconvolutionSignal processingPattern recognition (psychology)Component (thermodynamics)Function (biology)MathematicsArtificial intelligence
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Publication Info
- Year
- 2002
- Type
- article
- Volume
- 5
- Pages
- 3917-3920
- Citations
- 200
- Access
- Closed
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Cite This
Aapo Hyvärinen
(2002).
A family of fixed-point algorithms for independent component analysis.
, 5
, 3917-3920.
https://doi.org/10.1109/icassp.1997.604766
Identifiers
- DOI
- 10.1109/icassp.1997.604766