A diffusion strategy for distributed dictionary learning - Archive ouverte HAL Access content directly
Conference Papers Year : 2014

A diffusion strategy for distributed dictionary learning


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.
Fichier principal
Vignette du fichier
Chainais_itwist14_paper.pdf (194.55 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01104781 , version 1 (19-01-2015)



Pierre Chainais, Cédric Richard. A diffusion strategy for distributed dictionary learning. 2nd "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14), Laurent Jacques, Aug 2014, Namur, Belgium. ⟨hal-01104781⟩
401 View
144 Download



Gmail Facebook X LinkedIn More