Learning a common dictionary over a sensor network
Document type :
Communication dans un congrès avec actes
Title :
Learning a common dictionary over a sensor network
Author(s) :
Chainais, Pierre [Auteur]
Sequential Learning [SEQUEL]
Centrale Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Richard, Cédric [Auteur]
Joseph Louis LAGRANGE [LAGRANGE]

Sequential Learning [SEQUEL]
Centrale Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Richard, Cédric [Auteur]
Joseph Louis LAGRANGE [LAGRANGE]
Conference title :
CAMSAP 2013
City :
Saint-Martin
Country :
France
Start date of the conference :
2013-12-15
Publication date :
2013-12-15
English keyword(s) :
block coordinate descent
dictionary learning
sparse coding
distributed estimation
diffusion
matrix factorization
adaptive networks
block coordinate descent.
dictionary learning
sparse coding
distributed estimation
diffusion
matrix factorization
adaptive networks
block coordinate descent.
HAL domain(s) :
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/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]/Apprentissage [cs.LG]
English abstract : [en]
We consider the problem of distributed dictionary learning, where a set of nodes is required to collectively learn a common dictionary from noisy measurements. This approach may be useful in several contexts including ...
Show more >We consider the problem of distributed dictionary learning, where a set of nodes is required to collectively learn a common dictionary from noisy measurements. This approach may be useful in several contexts including sensor networks. Diffusion cooperation schemes have been proposed to solve the distributed linear regression problem. In this work we focus on a diffusion-based adaptive dictionary learning strategy: each node records independent observations and cooperates with its neighbors by sharing its local dictionary. The resulting algorithm corresponds to a distributed alternate optimization. Beyond dictionary learning, this strategy could be adapted to many matrix factorization problems in various settings. We illustrate its efficiency on some numerical experiments.Show less >
Show more >We consider the problem of distributed dictionary learning, where a set of nodes is required to collectively learn a common dictionary from noisy measurements. This approach may be useful in several contexts including sensor networks. Diffusion cooperation schemes have been proposed to solve the distributed linear regression problem. In this work we focus on a diffusion-based adaptive dictionary learning strategy: each node records independent observations and cooperates with its neighbors by sharing its local dictionary. The resulting algorithm corresponds to a distributed alternate optimization. Beyond dictionary learning, this strategy could be adapted to many matrix factorization problems in various settings. We illustrate its efficiency on some numerical experiments.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Comment :
4 pages
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