DocumentCode
3379599
Title
ICAR: independent component analysis using redundancies
Author
Albera, Laurent ; Ferreol, Anne ; Chevalier, Pascal ; Comon, Pierre
Author_Institution
I3S, Sophia-Antipolis, France
Volume
5
fYear
2004
fDate
23-26 May 2004
Abstract
A new blind source separation (BSS) algorithm, called ICAR and using only fourth order (FO) statistics of the data is proposed. The latter method is compared by computer experiments with the well-known methods COM1, COM2, JADE, FastICA, and SOBI. Since ICAR has given very good convergence results and has performed the source separation in the presence of the Gaussian noise with unknown spatial correlation, it appears as being one of the most attracting BSS algorithms.
Keywords
Gaussian noise; blind source separation; independent component analysis; redundancy; COM1; COM2; FastICA; Gaussian noise; ICAR; JADE; SOBI; blind source separation; fourth order data statistics; independent component analysis; redundancies; spatial correlation; Biosensors; Blind source separation; Decorrelation; Gaussian noise; Higher order statistics; Independent component analysis; Principal component analysis; Sensor arrays; Source separation; Statistical analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 2004. ISCAS '04. Proceedings of the 2004 International Symposium on
Print_ISBN
0-7803-8251-X
Type
conf
DOI
10.1109/ISCAS.2004.1329897
Filename
1329897
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