Name
Title | ||
|---|---|---|
Joint Topology Learning and Graph Signal Recovery Using Variational Bayes in Non-Gaussian Noise |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCSII.2021.3109339 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS |
Keywords | DocType | Volume |
Topology, Laplace equations, Signal processing algorithms, Noise measurement, Symmetric matrices, Heuristic algorithms, Computational modeling, Graph signal recovery, topology learning, Laplacian matrix, variational Bayes, non-Gaussian noise | Journal | 69 |
Issue | ISSN | Citations |
3 | 1549-7747 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Razieh Torkamani | 1 | 2 | 1.72 |
| Hadi Zayyani | 2 | 0 | 0.34 |
| Farokh Marvasti | 3 | 573 | 72.71 |