Graph Topology Learning and Signal Recovery Via Bayesian Inference

Mahmoud Ramezani-Mayiami, Mohammad Hajimirsadeghi, Karl Skretting, Rick S. Blum, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The estimation of a meaningful affinity graph has become a crucial task for representation of data, since the underlying structure is not readily available in many applications. In this paper, a topology inference framework, called Bayesian Topology Learning, is proposed to estimate the underlying graph topology from a given set of noisy measurements of signals. It is assumed that the graph signals are generated from Gaussian Markov Random Field processes. First, using a factor analysis model, the noisy measured data is represented in a latent space and its posterior probability density function is found. Thereafter, by utilizing the minimum mean square error estimator and the Expectation Maximization (EM) procedure, a filter is proposed to recover the signal from noisy measurements and an optimization problem is formulated to estimate the underlying graph topology. The experimental results show that the proposed method has better performance when compared to the current state-of-the-art algorithms with different performance measures.

Original languageEnglish (US)
Title of host publication2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages52-56
Number of pages5
ISBN (Electronic)9781728107080
DOIs
StatePublished - Jun 2019
Event2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States
Duration: Jun 2 2019Jun 5 2019

Publication series

Name2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Conference

Conference2019 IEEE Data Science Workshop, DSW 2019
CountryUnited States
CityMinneapolis
Period6/2/196/5/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality
  • Computational Theory and Mathematics
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Graph Topology Learning and Signal Recovery Via Bayesian Inference'. Together they form a unique fingerprint.

Cite this