DocumentCode :
774608
Title :
Identifiability of post-nonlinear mixtures
Author :
Achard, Sophie ; Jutten, Christian
Author_Institution :
Lab. of Comput. & Modeling, Univ. J. Fourier, Grenoble, France
Volume :
12
Issue :
5
fYear :
2005
fDate :
5/1/2005 12:00:00 AM
Firstpage :
423
Lastpage :
426
Abstract :
This letter deals with the resolution of the blind source separation problem using the independent component analysis method in post-nonlinear mixtures. Using the sole hypothesis of the source independence is not obvious to reconstruct the sources in nonlinear mixtures. Here, we prove the identifiability under weak assumptions on the mixture matrix and density sources.
Keywords :
blind source separation; independent component analysis; ICA; blind source separation; independent component analysis; post-nonlinear mixtures; Blind source separation; Brain mapping; Computational modeling; Gaussian noise; Image reconstruction; Independent component analysis; Input variables; Jacobian matrices; Laboratories; Source separation; Blind source separation; identifiability; independent component analysis (ICA); post nonlinear mixture;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2005.845593
Filename :
1420356
Link To Document :
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