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Communication Dans Un Congrès Année : 2019

A Data Augmentation Approach for Sampling Gaussian Models in High Dimension

Yosra Marnissi
  • Fonction : Auteur
Dany Abboud
  • Fonction : Auteur
Emilie Chouzenoux
Mohamed El-Badaoui

Résumé

Recently, methods based on Data Augmentation (DA) strategies have shown their efficiency for dealing with high-dimensional Gaussian sampling within Gibbs samplers compared to iterative-based sampling (e.g., Perturbation-Optimization). However, they are limited by the feasibility of the direct sampling of the auxiliary variable. This paper reviews DA sampling algorithms for Gaussian sampling and proposes a DA method which is especially useful when direct sampling of the auxiliary variable is not straightforward from a computational viewpoint. Experiments in two vibration analysis applications show the good performance of the proposed algorithm.
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Dates et versions

hal-02314418 , version 1 (12-10-2019)

Identifiants

Citer

Yosra Marnissi, Dany Abboud, Emilie Chouzenoux, Jean-Christophe Pesquet, Mohamed El-Badaoui, et al.. A Data Augmentation Approach for Sampling Gaussian Models in High Dimension. EUSIPCO 2019 - 27th European Signal Processing Conference, Sep 2019, La Corogne, Spain. ⟨10.23919/eusipco.2019.8902496⟩. ⟨hal-02314418⟩
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