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Title
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Joint Topology Learning and Graph Signal Recovery Using Variational Bayes in Non-Gaussian Noise
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Type
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JournalPaper
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Keywords
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Graph signal recovery, topology learning, Laplacian matrix, variational Bayes, non-Gaussian noise
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Abstract
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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.
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Researchers
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Hadi Zayyani (Second Researcher), Razieh Torkamani (First Researcher)
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