Bayesian Compressive Sensing for Clustered Sparse Signals

Yu, Lei; Sun, Hong; Barbot, Jean-Pierre; Zheng, Gang

Document type :

Communication dans un congrès avec actes

Title :

Bayesian Compressive Sensing for Clustered Sparse Signals

Author(s) :

Yu, Lei [Auteur]
Algebra for Digital Identification and Estimation [ALIEN]
Électronique et Commande des Systèmes Laboratoire [ECS-Lab]
Sun, Hong [Auteur]
Barbot, Jean-Pierre [Auteur]
Algebra for Digital Identification and Estimation [ALIEN]
Électronique et Commande des Systèmes Laboratoire [ECS-Lab]
Zheng, Gang [Auteur]

Non-Asymptotic estimation for online systems [NON-A]

Conference title :

ICASSP

City :

Prague

Country :

République tchèque

Start date of the conference :

2011-05-22

HAL domain(s) :

Informatique [cs]/Traitement des images [eess.IV]

English abstract : [en]

In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Besides sparse prior, cluster prior is ...
Show more >In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Besides sparse prior, cluster prior is introduced in this paper in order to investigate a class of structural sparse signals, called clustered sparse signals. A hierarchical statistical model is employed via Bayesian approach to model both the sparse prior and cluster prior and Markov Chain Monte Carlo (MCMC) sampling is implemented for the inference. Unlike the state-of-the-art algorithms based on the cluster prior, the proposed algorithm solves the inverse problem without any prior knowledge of the cluster parameters, even without the knowledge of the sparsity. The experimental results show that the proposed algorithm outperforms many state-of-the-art algorithms.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Non spécifiée

Popular science :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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