Adaptive Bayesian Estimation with Cluster ...
Document type :
Article dans une revue scientifique: Article original
DOI :
Title :
Adaptive Bayesian Estimation with Cluster Structured Sparsity
Author(s) :
Yu, Lei [Auteur]
Wuhan University [China]
Wei, Chen [Auteur]
Wuhan University [China]
Zheng, Gang [Auteur]
Non-Asymptotic estimation for online systems [NON-A]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Wuhan University [China]
Wei, Chen [Auteur]
Wuhan University [China]
Zheng, Gang [Auteur]

Non-Asymptotic estimation for online systems [NON-A]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Journal title :
IEEE Signal Processing Letters
Publisher :
Institute of Electrical and Electronics Engineers
Publication date :
2015
ISSN :
1070-9908
English keyword(s) :
Index Terms—Adaptive estimation
Bayesian inference
block sparsity
cluster structured sparsity
Bayesian inference
block sparsity
cluster structured sparsity
HAL domain(s) :
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
English abstract : [en]
—Armed with structures, group sparsity can be exploited to extraordinarily improve the performance of adaptive estimation. In this letter, the adaptive estimation algorithm for cluster structured sparse signals, called ...
Show more >—Armed with structures, group sparsity can be exploited to extraordinarily improve the performance of adaptive estimation. In this letter, the adaptive estimation algorithm for cluster structured sparse signals, called A-CluSS, is proposed. In particular, a hierarchical Bayesian model is built, where both sparse prior and cluster structured prior are exploited simultaneously. The adaptive updating formulas for statistical variables are obtained via the variational Bayesian inference and the resulted algorithms can adaptively estimate the cluster structured sparse signals without knowledge of block size, block numbers and block locations. Superiority of proposed A-CluSS is demonstrated via various simulations.Show less >
Show more >—Armed with structures, group sparsity can be exploited to extraordinarily improve the performance of adaptive estimation. In this letter, the adaptive estimation algorithm for cluster structured sparse signals, called A-CluSS, is proposed. In particular, a hierarchical Bayesian model is built, where both sparse prior and cluster structured prior are exploited simultaneously. The adaptive updating formulas for statistical variables are obtained via the variational Bayesian inference and the resulted algorithms can adaptively estimate the cluster structured sparse signals without knowledge of block size, block numbers and block locations. Superiority of proposed A-CluSS is demonstrated via various simulations.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
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