DocumentCode :
86915
Title :
General Non-Orthogonal Constrained ICA
Author :
Rodriguez, Pedro A. ; Anderson, Matthew ; Xi-Lin Li ; Adali, Tulay
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Maryland, Baltimore, MD, USA
Volume :
62
Issue :
11
fYear :
2014
fDate :
1-Jun-14
Firstpage :
2778
Lastpage :
2786
Abstract :
Constrained independent component analysis (C-ICA) algorithms have been an effective way to introduce prior information into the ICA framework. The work in this area has focus on adding constraints to the objective function of algorithms that assume an orthogonal demixing matrix. Orthogonality is required in order to decouple-isolate-the constraints applied for each individual source. This assumption limits the optimization space and therefore the separation performance of C-ICA algorithms. We generalize the existing C-ICA framework by using a novel decoupling method that preserves the larger optimization space for the demixing matrix. In addition, this framework allows for the constraining of either the sources or the mixing coefficients. A constrained version of the extended Infomax algorithm is used as an example to show the benefits obtained from the non-orthogonal constrained framework we introduce.
Keywords :
blind source separation; independent component analysis; matrix algebra; maximum likelihood estimation; C-ICA algorithms; constrained independent component analysis algorithms; extended Infomax algorithm; mixing coefficients; novel decoupling method; optimization space; orthogonal demixing matrix; prior information; Algorithm design and analysis; Data models; Linear programming; Matrix decomposition; Optimization; Signal processing algorithms; Vectors; Constrained ICA; decoupled; maximum likelihood;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2014.2318136
Filename :
6802439
Link To Document :
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