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A family of fixed-point algorithms for independent component analysis

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1 Author(s)
Hyvarinen, A. ; Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Espoo, Finland

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

Published in:

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on  (Volume:5 )

Date of Conference:

21-24 Apr 1997

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