TY - JOUR
T1 - A new information-theoretic based ICA algorithm for blind signal separation
AU - Cheung, Yiu Ming
AU - Xu, Lei
N1 - Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2003
Y1 - 2003
N2 - The typical information-theoretic approaches such as INFOMAX and MMI perform independent component analysis (ICA) by using a fixed nonlinearity function. Consequently, they can only separate either sub-Gaussian or super-Gaussian source signals, but not both. This article considers a flexible nonlinearity function that is a single polynomial term with the exponent learnable. The separation ability of this function is analysed, and a new ICA algorithm is proposed. The experiments have shown that this algorithm can successfully separate the mixture of sub-Gaussian and super-Gaussian sources.
AB - The typical information-theoretic approaches such as INFOMAX and MMI perform independent component analysis (ICA) by using a fixed nonlinearity function. Consequently, they can only separate either sub-Gaussian or super-Gaussian source signals, but not both. This article considers a flexible nonlinearity function that is a single polynomial term with the exponent learnable. The separation ability of this function is analysed, and a new ICA algorithm is proposed. The experiments have shown that this algorithm can successfully separate the mixture of sub-Gaussian and super-Gaussian sources.
KW - Blind signal separation
KW - Flexible nonlinearity function
KW - Independent component analysis
KW - Sub-Gaussian and super-Gaussian sources
UR - http://www.scopus.com/inward/record.url?scp=0037262062&partnerID=8YFLogxK
U2 - 10.1080/1206212X.2003.11441691
DO - 10.1080/1206212X.2003.11441691
M3 - Journal article
AN - SCOPUS:0037262062
SN - 1206-212X
VL - 25
SP - 106
EP - 110
JO - International Journal of Computers and Applications
JF - International Journal of Computers and Applications
IS - 2
ER -