Adaptive Bayesian Estimation with Cluster ...
Type de document :
Article dans une revue scientifique: Article original
DOI :
Titre :
Adaptive Bayesian Estimation with Cluster Structured Sparsity
Auteur(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]
Titre de la revue :
IEEE Signal Processing Letters
Éditeur :
Institute of Electrical and Electronics Engineers
Date de publication :
2015
ISSN :
1070-9908
Mot(s)-clé(s) en anglais :
Index Terms—Adaptive estimation
Bayesian inference
block sparsity
cluster structured sparsity
Bayesian inference
block sparsity
cluster structured sparsity
Discipline(s) HAL :
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Résumé en anglais : [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 ...
Lire la suite >—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.Lire moins >
Lire la suite >—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.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
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