Adaptive Bayesian Estimation with Cluster Structured Sparsity

Lei Yu (1) , Chen Wei (1) , Gang Zheng (2, 3)

1 Wuhan University [China]
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 NON-A - Non-Asymptotic estimation for online systems

—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.

Mots clés: Index Terms—Adaptive estimation; Bayesian inference; block sparsity; cluster structured sparsity

Domaines: Traitement du signal et de l'image [eess.SP]

Lei Yu, Chen Wei, Gang Zheng. Adaptive Bayesian Estimation with Cluster Structured Sparsity. IEEE Signal Processing Letters, 2015, ⟨10.1109/LSP.2015.2477440⟩. ⟨hal-01252325⟩