Online Time-Varying Topology Identification Via Prediction-Correction Algorithms

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Abstract

Signal processing and machine learning algorithms for data sup-ported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of the problem (e.g., water supply networks, power grids), the topology has to be learned from data. Topology identification is a challenging task, as the problem is often ill-posed, and becomes even harder when the graph structure is time-varying. In this paper, we address the problem of dynamic topology identification by building on recent results from time-varying optimization, devising a general-purpose online algorithm operating in non-stationary environments. Because of its iteration-constrained nature, the proposed approach exhibits an intrinsic temporal-regularization of the graph topology without explicitly enforcing it. As a case-study, we specialize our method to the Gaussian graphical model (GGM) problem and corroborate its performance.
Original languageEnglish
Title of host publicationICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Place of PublicationPiscataway
PublisherIEEE
Pages5400-5404
Number of pages5
ISBN (Electronic)978-1-7281-7605-5
ISBN (Print)978-1-7281-7606-2
DOIs
Publication statusPublished - 2021
EventICASSP 2021: The IEEE International Conference on Acoustics, Speech, and Signal Processing - Virtual Conference/Toronto, Canada
Duration: 6 Jun 202111 Jun 2021

Conference

ConferenceICASSP 2021
CountryCanada
CityVirtual Conference/Toronto
Period6/06/2111/06/21

Keywords

  • dynamic topology identification
  • online algorithm
  • graphical models
  • graph learning
  • time-varying optimization

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