Title :

A diffusion strategy for distributed dictionary learning

Author(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]

Conference title :

2nd "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

Conference organizers(s) :

Laurent Jacques

City :

Namur

Country :

Belgique

Start date of the conference :

2014-08-27

Journal title :

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

Publication date :

2014-10-02

HAL domain(s) :

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]

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL