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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Vydané v:	IEEE signal processing letters Ročník 28; s. 2192 - 2196
Hlavní autori:	Saboksayr, Seyed Saman, Mateos, Gonzalo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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