Accelerated Graph Learning From Smooth Signals

doi:10.1109/LSP.2021.3123459

Accelerated Graph Learning From Smooth Signals

2021-01-01

16

OA

AI

S

Seyed Saman Saboksayr

G

Gonzalo Mateos *

DOI：10.1109/LSP.2021.3123459

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Abstract

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We consider network topology identification subject to a signal smoothness prior on the nodal observations. A fast dual-based proximal gradient algorithm is developed to efficiently tackle a strongly convex, smoothness-regularized network inverse problem known to yield high-quality graph solutions. Unlike existing solvers, the novel iterations come with global convergence rate guarantees and do not require additional step-size tuning. Reproducible simulated tests demonstrate the effectiveness of the proposed method in accurately recovering random and real-world graphs, markedly faster than state-of-the-art alternatives and without incurring an extra computational burden.

Keywords:

Signal processing algorithms

Convergence

Topology

Network topology

Inference algorithms

Convex functions

Tuning

Graph learning

graph signal processing

fast gradient methods

signal smoothness

topology identification

Accelerated Graph Learning From Smooth Signals

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