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Distributed dictionary learning over a ...
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Document type :
Communication dans un congrès avec actes
Title :
Distributed dictionary learning 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]
Conference title :
CaP 2013
City :
Villeneuve d'Ascq
Country :
France
Start date of the conference :
2013-07-02
Publication date :
2013-07-02
English keyword(s) :
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]
English abstract : [en]
We consider the problem of distributed dictionary learning, where a set of nodes is required to collec- tively learn a common dictionary from noisy measure- ments. This approach may be useful in several con- texts including ...
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We consider the problem of distributed dictionary learning, where a set of nodes is required to collec- tively learn a common dictionary from noisy measure- ments. This approach may be useful in several con- texts 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 observations and cooperates with its neighbors by sharing its local dictionary. The resulting algorithm corresponds to a distributed block coordi- nate descent (alternate optimization). Beyond dictio- nary learning, this strategy could be adapted to many matrix factorization problems and generalized to var- ious settings. This article presents our approach and illustrates its efficiency on some numerical examples.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Nationale
Popular science :
Non
Comment :
6 pages
Collections :
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Source :
Harvested from HAL
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