Graph recursive least squares filter for topology inference in causal data processes

Ramezani-Mayiami, Mahmoud; Beferull-Lozano, Baltasar

doi:10.1109/camsap.2017.8313212

Cited by 5 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, one of the desired goals of the GSP framework is estimating the underlying topology given a set of measured data. Some researches concentrated on directed topology estimation [4][5][6][7][8][9][10][11][12][13][14][15][16], where specific process models have been used which are suitable for limited applications. However, the Gaussian model is a ubiquitous signal model which can be utilized for a wide range of applications.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Topology Learning and noise removal from network data

Mayiami

Hajimirsadeghi

Skretting

et al. 2021

Discov Internet Things

View full text Add to dashboard Cite

Learning the topology of a graph from available data is of great interest in many emerging applications. Some examples are social networks, internet of things networks (intelligent IoT and industrial IoT), biological connection networks, sensor networks and traffic network patterns. In this paper, a graph topology inference approach is proposed to learn the underlying graph structure from a given set of noisy multi-variate observations, which are modeled as graph signals generated from a Gaussian Markov Random Field (GMRF) process. A factor analysis model is applied to represent the graph signals in a latent space where the basis is related to the underlying graph structure. An optimal graph filter is also developed to recover the graph signals from noisy observations. In the final step, an optimization problem is proposed to learn the underlying graph topology from the recovered signals. Moreover, a fast algorithm employing the proximal point method has been proposed to solve the problem efficiently. Experimental results employing both synthetic and real data show the effectiveness of the proposed method in recovering the signals and inferring the underlying graph.

show abstract