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