Accelerated Graph Learning from Smooth Signals

2021/10/19 by Saboksayr, Seyed Saman, Mateos, Gonzalo · 4 citations
#FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2110.09677

Abstract

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.

Cited by

• Topology Identification and Inference over Graphs
• Non-negative DAG Learning from Time-Series Data
• Estimating Fair Graphs from Graph-Stationary Data
• Manifold-regularised Large-Margin ℓ_p-SVDD for Multidimensional Time Series Anomaly Detection

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