IEICE TRANSACTIONS on Information
Sparsity Regularized Affine Projection Adaptive Filtering for System Identification
Summary :
A new type of the affine projection (AP) algorithms which incorporates the sparsity condition of a system is presented. To exploit the sparsity of the system, a weighted l1-norm regularization is imposed on the cost function of the AP algorithm. Minimizing the cost function with a subgradient calculus and choosing two distinct weightings for l1-norm, two stochastic gradient based sparsity regularized AP (SR-AP) algorithms are developed. Experimental results show that the SR-AP algorithms outperform the typical AP counterparts for identifying sparse systems.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.4 pp.964-967
- Publication Date
- 2014/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E97.D.964
- Type of Manuscript
- LETTER
- Category
- Fundamentals of Information Systems
Authors
Young-Seok CHOI
Gangneung-Wonju National University
Keyword
Latest Issue
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Young-Seok CHOI, "Sparsity Regularized Affine Projection Adaptive Filtering for System Identification" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 4, pp. 964-967, April 2014, doi: 10.1587/transinf.E97.D.964.
Abstract: A new type of the affine projection (AP) algorithms which incorporates the sparsity condition of a system is presented. To exploit the sparsity of the system, a weighted l1-norm regularization is imposed on the cost function of the AP algorithm. Minimizing the cost function with a subgradient calculus and choosing two distinct weightings for l1-norm, two stochastic gradient based sparsity regularized AP (SR-AP) algorithms are developed. Experimental results show that the SR-AP algorithms outperform the typical AP counterparts for identifying sparse systems.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.964/_p
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@ARTICLE{e97-d_4_964,
author={Young-Seok CHOI, },
journal={IEICE TRANSACTIONS on Information},
title={Sparsity Regularized Affine Projection Adaptive Filtering for System Identification},
year={2014},
volume={E97-D},
number={4},
pages={964-967},
abstract={A new type of the affine projection (AP) algorithms which incorporates the sparsity condition of a system is presented. To exploit the sparsity of the system, a weighted l1-norm regularization is imposed on the cost function of the AP algorithm. Minimizing the cost function with a subgradient calculus and choosing two distinct weightings for l1-norm, two stochastic gradient based sparsity regularized AP (SR-AP) algorithms are developed. Experimental results show that the SR-AP algorithms outperform the typical AP counterparts for identifying sparse systems.},
keywords={},
doi={10.1587/transinf.E97.D.964},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Sparsity Regularized Affine Projection Adaptive Filtering for System Identification
T2 - IEICE TRANSACTIONS on Information
SP - 964
EP - 967
AU - Young-Seok CHOI
PY - 2014
DO - 10.1587/transinf.E97.D.964
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2014
AB - A new type of the affine projection (AP) algorithms which incorporates the sparsity condition of a system is presented. To exploit the sparsity of the system, a weighted l1-norm regularization is imposed on the cost function of the AP algorithm. Minimizing the cost function with a subgradient calculus and choosing two distinct weightings for l1-norm, two stochastic gradient based sparsity regularized AP (SR-AP) algorithms are developed. Experimental results show that the SR-AP algorithms outperform the typical AP counterparts for identifying sparse systems.
ER -
English
