DocumentCode
959399
Title
Identifiability issues in noisy ICA
Author
Davies, Mike
Author_Institution
DSP Group, Univ. of London, UK
Volume
11
Issue
5
fYear
2004
fDate
5/1/2004 12:00:00 AM
Firstpage
470
Lastpage
473
Abstract
We consider the identifiability of the statistical model for noisy independent component analysis showing that while the mixing process is identifiable, the noise covariance is only partially so. This raises questions as to the performance of certain maximum-likelihood algorithms for blind source separation in the presence of noise.
Keywords
blind source separation; independent component analysis; maximum likelihood estimation; signal denoising; signal reconstruction; signal sources; blind source separation; maximum-likelihood algorithms; noise covariance; noisy ICA; noisy independent component analysis; statistical model identifiability; Background noise; Blind source separation; Covariance matrix; Digital signal processing; Gaussian noise; Helium; Higher order statistics; Independent component analysis; Noise reduction; Source separation;
fLanguage
English
Journal_Title
Signal Processing Letters, IEEE
Publisher
ieee
ISSN
1070-9908
Type
jour
DOI
10.1109/LSP.2004.826508
Filename
1288110
Link To Document