Sparse Graph Signal Recovery by the Graph-Based Multiple Generalized Information Criterion (GM-GIC)

Gal Morgenstern, Tirza Routtenberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper investigates the recovery of sparse signals over graphs, which is a common problem in graph signal processing (GSP) applications such as anomaly detection in sensor networks. We represent the sparse graph signals as a graph fil-ter output and pose the problem as hypothesis testing. Based on this representation, we propose the Graph-Based Multiple Generalized Information Criterion (GM-GIC), which leverages the double sparsity of the graph signal and the graph filter. In the first stage of the GM-GIC method, we test each dictionary element (graph filter matrix column) to identify if it captures information on the sparse signal. Next, we partition the subset of informative dictionary elements into smaller subsets that span orthogonal subspaces. Finally, we compute the local GICs over each subset and combine them into a global decision. Simulations show that the GM-GIC method improves the support recovery performance compared with existing methods without significant computational overhead.

Original languageEnglish
Title of host publication2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
PublisherInstitute of Electrical and Electronics Engineers
Pages491-495
Number of pages5
ISBN (Electronic)9798350344523
DOIs
StatePublished - 1 Jan 2023
Event9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023 - Herradura, Costa Rica
Duration: 10 Dec 202313 Dec 2023

Publication series

Name2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023

Conference

Conference9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
Country/TerritoryCosta Rica
CityHerradura
Period10/12/2313/12/23

Keywords

  • double sparsity
  • generalized information criterion (GIC)
  • Sparse signal estimation
  • support recovery

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Signal Processing
  • Control and Optimization
  • Instrumentation

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