Title :
Identifiability of post-nonlinear mixtures
Author :
Achard, Sophie ; Jutten, Christian
Author_Institution :
Lab. of Comput. & Modeling, Univ. J. Fourier, Grenoble, France
fDate :
5/1/2005 12:00:00 AM
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;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2005.845593