A diffusion strategy for distributed ...
Type de document :
Communication dans un congrès avec actes
Titre :
A diffusion strategy for distributed dictionary learning
Auteur(s) :
Chainais, Pierre [Auteur]
Centrale Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
LAGIS-SI
Sequential Learning [SEQUEL]
Richard, Cédric [Auteur]
Joseph Louis LAGRANGE [LAGRANGE]
Centrale Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
LAGIS-SI
Sequential Learning [SEQUEL]
Richard, Cédric [Auteur]
Joseph Louis LAGRANGE [LAGRANGE]
Titre de la manifestation scientifique :
2nd "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
Organisateur(s) de la manifestation scientifique :
Laurent Jacques
Ville :
Namur
Pays :
Belgique
Date de début de la manifestation scientifique :
2014-08-27
Titre de la revue :
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
Date de publication :
2014-10-02
Discipline(s) HAL :
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Apprentissage [cs.LG]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Apprentissage [cs.LG]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Résumé en anglais : [en]
We consider the problem of a set of nodes which is required to collectively learn a common dictionary from noisy measurements. This distributed dictionary learning approach may be useful in several contexts including sensor ...
Lire la suite >We consider the problem of a set of nodes which is required to collectively learn a common dictionary from noisy measurements. This distributed dictionary learning approach may be useful in several contexts including sensor networks. Dif-fusion cooperation schemes have been proposed to estimate a consensus solution to distributed linear regression. This work proposes a diffusion-based adaptive dictionary learning strategy. Each node receives measurements which may be shared or not with its neighbors. All nodes cooperate with their neighbors by sharing their local dictionary to estimate a common representa-tion. In a diffusion approach, the resulting algorithm corresponds to a distributed alternate optimization. Beyond dictionary learn-ing, this strategy could be adapted to many matrix factorization problems in various settings. We illustrate its efficiency on some numerical experiments, including the difficult problem of blind hyperspectral images unmixing.Lire moins >
Lire la suite >We consider the problem of a set of nodes which is required to collectively learn a common dictionary from noisy measurements. This distributed dictionary learning approach may be useful in several contexts including sensor networks. Dif-fusion cooperation schemes have been proposed to estimate a consensus solution to distributed linear regression. This work proposes a diffusion-based adaptive dictionary learning strategy. Each node receives measurements which may be shared or not with its neighbors. All nodes cooperate with their neighbors by sharing their local dictionary to estimate a common representa-tion. In a diffusion approach, the resulting algorithm corresponds to a distributed alternate optimization. Beyond dictionary learn-ing, this strategy could be adapted to many matrix factorization problems in various settings. We illustrate its efficiency on some numerical experiments, including the difficult problem of blind hyperspectral images unmixing.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
Fichiers
- https://hal.archives-ouvertes.fr/hal-01104781/document
- Accès libre
- Accéder au document
- http://arxiv.org/pdf/1410.0719
- Accès libre
- Accéder au document
- https://hal.archives-ouvertes.fr/hal-01104781/document
- Accès libre
- Accéder au document
- https://hal.archives-ouvertes.fr/hal-01104781/document
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- Chainais_itwist14_paper.pdf
- Accès libre
- Accéder au document
- 1410.0719
- Accès libre
- Accéder au document