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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2022

04.01.2022

Distributed Analysis Dictionary Learning Using a Diffusion Strategy

verfasst von: Jing Dong, Liu Yang, Chang Liu, Xiaoqing Luo, Jian Guan

Erschienen in: Neural Processing Letters | Ausgabe 3/2022

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Abstract

We consider the problem of distributed dictionary learning which aims to learn a global dictionary from data geographically distributed on nodes of a network. Existing works are based on sparse synthesis model while this paper is based on sparse analysis model. Two novel distributed analysis dictionary learning (ADL) algorithms are proposed by adapting the centralized ADL algorithms Analysis SimCO (ASimCO) and Incoherent Analysis SimCO (INASimCO) to distributed settings. In particular, local representation vectors and local dictionaries are introduced, and they can be updated independently on each node by distributing the sparse coding and dictionary update stages of ASimCO. A diffusion strategy is then applied to estimate a global dictionary from the local dictionaries by exchanging local information. Experimental results with synthetic data and for image denoising demonstrate that the proposed distributed ADL algorithms can obtain similar results as correpsonding centralized algorithms.
Vorheriger Artikel Correlated Initialization for Correlated Data
Nächster Artikel Effects of Similarity Score Functions in Attention Mechanisms on the Performance of Neural Question Answering Systems

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Fußnoten
1
All simulations were performed in Matlab 2014a with an Intel Core i7 CPU at 2.40 GHz and 12 GB memory.
 
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Metadaten
Titel
Distributed Analysis Dictionary Learning Using a Diffusion Strategy
verfasst von
Jing Dong
Liu Yang
Chang Liu
Xiaoqing Luo
Jian Guan
Publikationsdatum
04.01.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10729-x

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