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Learning a common dictionary over a sensor network

Pierre Chainais 1, 2, 3 Cédric Richard 4
3 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : 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.
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Submitted on : Saturday, January 4, 2014 - 12:58:50 PM
Last modification on : Tuesday, October 19, 2021 - 7:00:54 PM
Long-term archiving on: : Friday, April 4, 2014 - 10:10:35 PM


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  • HAL Id : hal-00923742, version 1


Pierre Chainais, Cédric Richard. Learning a common dictionary over a sensor network. CAMSAP 2013, Dec 2013, Saint-Martin, France. pp.1-4. ⟨hal-00923742⟩



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