Accelerated Graph Learning From Smooth Signals

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....

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Bibliographic Details
Published in:IEEE signal processing letters Vol. 28; pp. 2192 - 2196
Main Authors: Saboksayr, Seyed Saman, Mateos, Gonzalo
Format: Journal Article
Language:English
Published: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Convergence
Convex functions
fast gradient methods
Gradient methods
Graph learning
graph signal processing
Graph theory
Inference algorithms
Inverse problems
Network topologies
Network topology
Signal processing algorithms
signal smoothness
Smoothness
topology identification
ISSN:1070-9908, 1558-2361
Online Access:Get full text
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Summary: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.
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2021.3123459