On Graph Uncertainty Principle and Eigenvector Delocalization

Elizaveta Rebrova, Palina Salanevich

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Uncertainty principles present an important theoretical tool in signal processing, as they provide limits on the time-frequency concentration of a signal. In many real-world applications the signal domain has a complicated irregular structure that can be described by a graph. In this paper, we focus on the global uncertainty principle on graphs and propose new connections between the uncertainty bound for graph signals and graph eigenvectors delocalization. We also derive uncertainty bounds for random d-regular graphs and provide numerically efficient upper and lower approximations for the uncertainty bound on an arbitrary graph.

Original languageEnglish (US)
Title of host publication2023 International Conference on Sampling Theory and Applications, SampTA 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350328851
DOIs
StatePublished - 2023
Event2023 International Conference on Sampling Theory and Applications, SampTA 2023 - New Haven, United States
Duration: Jul 10 2023Jul 14 2023

Publication series

Name2023 International Conference on Sampling Theory and Applications, SampTA 2023

Conference

Conference2023 International Conference on Sampling Theory and Applications, SampTA 2023
Country/TerritoryUnited States
CityNew Haven
Period7/10/237/14/23

All Science Journal Classification (ASJC) codes

  • Discrete Mathematics and Combinatorics
  • Statistics and Probability
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Signal Processing

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