By Topic

A family of fixed-point algorithms for independent component analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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