Bayesian Pursuit Algorithms

Cédric Herzet 1, 2 Angélique Drémeau 1
1 TEMICS - Digital image processing, modeling and communication
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 FLUMINANCE - Fluid Flow Analysis, Description and Control from Image Sequences
CEMAGREF - Centre national du machinisme agricole, du génie rural, des eaux et forêts, Inria Rennes – Bretagne Atlantique
Abstract : This paper addresses the sparse representation (SR) problem within a general Bayesian framework. We show that the Lagrangian formulation of the standard SR problem can be regarded as a limit case of a general maximum a posteriori (MAP) problem involving Bernoulli-Gaussian variables. We then propose different tractable implementations of this MAP problem and explain several well-known pursuit algorithms (e.g., MP, OMP, StOMP, CoSaMP, SP) as particular cases of the proposed Bayesian formulation.
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https://hal.inria.fr/inria-00539109
Contributor : Angélique Drémeau <>
Submitted on : Wednesday, November 24, 2010 - 9:36:07 AM
Last modification on : Monday, September 2, 2019 - 2:20:10 PM

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  • HAL Id : inria-00539109, version 1

Citation

Cédric Herzet, Angélique Drémeau. Bayesian Pursuit Algorithms. Proc. European Signal Processing Conference (EUSIPCO), Aug 2010, Aalborg, Denmark. ⟨inria-00539109⟩

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