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Title: Graph Topology Learning and Signal Recovery Via Bayesian Inference
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 graphtopologyfromagivensetofnoisymeasurementsofsignals. It is assumed that the graph signals are generated from GaussianMarkovRandomFieldprocesses. First,usingafactor 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 estimatetheunderlyinggraphtopology. Theexperimentalresults show that the proposed method has better performance whencomparedtothecurrentstate-of-the-artalgorithmswith different performance measures.  more » « less
Award ID(s):
1702555
NSF-PAR ID:
10142727
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
2019 IEEE Data Science Workshop (DSW)
Page Range / eLocation ID:
52 to 56
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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