DocumentCode :
1890996
Title :
Pruning sparse signal models using interference
Author :
Sturm, Bob L. ; Shynk, John J. ; Kim, Dae Hong
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of California, Santa Barbara, CA
fYear :
2009
fDate :
18-20 March 2009
Firstpage :
454
Lastpage :
458
Abstract :
Previous work on sparse approximations has shown that in the pursuit of a signal model using greedy iterative algorithms, the efficiency of the representation can be increased by considering the interference between selected atoms. However, in such interference-adaptive algorithms, atoms are still often selected that necessitate correction by subsequently chosen atoms. It is thus logical to remove these atoms from the representation so that they do not diminish the efficiency of the pursued signal model. In this paper, we propose to prune atoms from the model based on the degree and type of interference, and test its effectiveness in an interference-adaptive orthogonal matching pursuit algorithm.
Keywords :
correlation methods; interference (signal); iterative methods; signal representation; correlation method; interference-adaptive orthogonal matching pursuit algorithm; prune atom; signal representation; sparse signal model; Covariance matrix; Error analysis; Interference; Mathematics; Maximum likelihood estimation; Parameter estimation; Physics; Reactive power; Statistical distributions; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Sciences and Systems, 2009. CISS 2009. 43rd Annual Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
978-1-4244-2733-8
Electronic_ISBN :
978-1-4244-2734-5
Type :
conf
DOI :
10.1109/CISS.2009.5054763
Filename :
5054763
Link To Document :
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