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Joint topology learning and graph signal recovery using variational bayes in Non-gaussian noise

Torkamani, R ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1109/TCSII.2021.3109339
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2022
  4. Abstract:
  5. This brief proposes a joint graph signal recovery and topology learning algorithm using a Variational Bayes (VB) framework in the case of non-Gaussian measurement noise. It is assumed that the graph signal is Gaussian Markov Random Field (GMRF) and the graph weights are considered statistical with the Gaussian prior. Moreover, the non-Gaussian noise is modeled using two distributions: Mixture of Gaussian (MoG), and Laplace. All the unknowns of the problem which are graph signal, Laplacian matrix, and the (Hyper)parameters are estimated by a VB framework. All the posteriors are calculated in closed forms and the iterative VB algorithm is devised to solve the problem. The efficiency of the proposed algorithm in comparison to some state-of-the-art algorithms in the literature is shown in the simulation results. © 2004-2012 IEEE
  6. Keywords:
  7. Laplacian matrix ; Barium compounds ; Gaussian distribution ; Iterative methods ; Laplace transforms ; Learning algorithms ; Markov processes ; Matrix algebra ; Recovery ; Signal reconstruction ; Topology ; Gaussian measurements ; Graph signal recovery ; Laplacian matrices ; Measurement Noise ; Non-Gaussian ; Non-Gaussian noise ; Signal recovery ; Topology graphs ; Topology learning ; Variational bayes ; Gaussian noise (electronic)
  8. Source: IEEE Transactions on Circuits and Systems II: Express Briefs ; Volume 69, Issue 3 , 2022 , Pages 1887-1891 ; 15497747 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/9526764