@inproceedings{37179b6272ab4d07adfd7e6dc39307c4,
title = "On Graph Uncertainty Principle and Eigenvector Delocalization",
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.",
author = "Elizaveta Rebrova and Palina Salanevich",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Sampling Theory and Applications, SampTA 2023 ; Conference date: 10-07-2023 Through 14-07-2023",
year = "2023",
doi = "10.1109/SampTA59647.2023.10301367",
language = "English (US)",
series = "2023 International Conference on Sampling Theory and Applications, SampTA 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 International Conference on Sampling Theory and Applications, SampTA 2023",
address = "United States",
}