DocumentCode :
3481105
Title :
Adaptive filtering algorithms for promoting sparsity
Author :
Rao, Bhaskar D. ; Song, Bongyong
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of California San Diego, La Jolla, CA, USA
Volume :
6
fYear :
2003
fDate :
6-10 April 2003
Abstract :
We provide a mathematical framework for developing adaptive filtering algorithms for exploiting/enforcing sparsity. The approach is based on minimizing a regularized mean squared error criterion with sparsity being promoted by the regularizing term which consists of a diversity measure. A steepest descent algorithm (SDA) is developed to minimize the regularized cost function. Then we extend the algorithm to the adaptive environment and develop a class of algorithms, which we term the pLMS algorithm class and which incudes important variants - pLLMS (leaky pLMS) and pNLMS (normalized pLMS). The framework is quite general and encompasses a broad range of adaptive algorithms with the pNLMS having similarity with the proportionate normalized least-mean-squares (PNLMS) algorithm. Computer simulations have been conducted using the echo canceller application as an example of a sparse environment. The simulations clearly show the ability of the developed algorithms to exploit the inherent sparsity structure, thereby outperforming conventional algorithms like the NLMS algorithm in this application.
Keywords :
adaptive filters; filtering theory; least mean squares methods; minimisation; adaptive filtering algorithms; diversity measure; echo canceller; proportionate normalized least-mean-squares algorithm; regularized mean squared error criterion minimization; sparsity structure; steepest descent algorithm; Adaptive algorithm; Adaptive filters; Algorithm design and analysis; Cost function; Echo cancellers; Filtering algorithms; Least squares approximation; Matching pursuit algorithms; Pursuit algorithms; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
0-7803-7663-3
Type :
conf
DOI :
10.1109/ICASSP.2003.1201693
Filename :
1201693
Link To Document :
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