Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

November 15, 1998, Vol. 10, No. 8, Pages 2103-2114
(doi: 10.1162/089976698300016981)
© 1998 Massachusetts Institute of Technology
An Alternative Perspective on Adaptive Independent Component Analysis Algorithms
Article PDF (927.96 KB)
Abstract

This article develops an extended independent component analysis algorithm for mixtures of arbitrary subgaussian and supergaussian sources. The gaussian mixture model of Pearson is employed in deriving a closed-form generic score function for strictly subgaussian sources. This is combined with the score function for a unimodal supergaussian density to provide a computationally simple yet powerful algorithm for performing independent component analysis on arbitrary mixtures of nongaussian sources.