Processus du Buffet Indien pour l'apprentissage ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Processus du Buffet Indien pour l'apprentissage de dictionnaire : algorithmes et applications en traitement d'image.
Auteur(s) :
Dang, Hong-Phuong [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Chainais, Pierre [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Centrale Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Chainais, Pierre [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Centrale Lille
Titre de la revue :
International Journal of Approximate Reasoning
Éditeur :
Elsevier
Date de publication :
2017-01
ISSN :
0888-613X
Mot(s)-clé(s) en anglais :
sparse representations
dictionary learning
inverse problems
Indian Buffet Process
Bayesian non parametric
dictionary learning
inverse problems
Indian Buffet Process
Bayesian non parametric
Discipline(s) HAL :
Mathématiques [math]/Statistiques [math.ST]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement des images [eess.IV]
Statistiques [stat]
Statistiques [stat]/Applications [stat.AP]
Statistiques [stat]/Machine Learning [stat.ML]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement des images [eess.IV]
Statistiques [stat]
Statistiques [stat]/Applications [stat.AP]
Statistiques [stat]/Machine Learning [stat.ML]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Résumé en anglais : [en]
Ill-posed inverse problems call for some prior model to define a suitable set of solutions. A wide family of approaches relies on the use of sparse representations. Dictionary learning precisely permits to learn a redundant ...
Lire la suite >Ill-posed inverse problems call for some prior model to define a suitable set of solutions. A wide family of approaches relies on the use of sparse representations. Dictionary learning precisely permits to learn a redundant set of atoms to represent the data in a sparse manner. Various approaches have been proposed, mostly based on optimization methods. We propose a Bayesian non parametric approach called IBP-DL that uses an Indian Buffet Process prior. This method yields an efficient dictionary with an adaptive number of atoms. Moreover the noise and sparsity levels are also inferred so that no parameter tuning is needed. We elaborate on the IBP-DL model to propose a model for linear inverse problems such as inpainting and compressive sensing beyond basic denoising. We derive a collapsed and an accelerated Gibbs samplers and propose a marginal maximum a posteriori estimator of the dictionary. Several image processing experiments are presented and compared to other approaches for illustration.Lire moins >
Lire la suite >Ill-posed inverse problems call for some prior model to define a suitable set of solutions. A wide family of approaches relies on the use of sparse representations. Dictionary learning precisely permits to learn a redundant set of atoms to represent the data in a sparse manner. Various approaches have been proposed, mostly based on optimization methods. We propose a Bayesian non parametric approach called IBP-DL that uses an Indian Buffet Process prior. This method yields an efficient dictionary with an adaptive number of atoms. Moreover the noise and sparsity levels are also inferred so that no parameter tuning is needed. We elaborate on the IBP-DL model to propose a model for linear inverse problems such as inpainting and compressive sensing beyond basic denoising. We derive a collapsed and an accelerated Gibbs samplers and propose a marginal maximum a posteriori estimator of the dictionary. Several image processing experiments are presented and compared to other approaches for illustration.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
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- IJAR_Dang_Chainais_2017.pdf
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