Topology-Aware Joint Graph Filter and Edge Weight Identification for Network Processes

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Abstract

Data defined over a network have been successfully modelled by means of graph filters. However, although in many scenarios the connectivity of the network is known, e.g., smart grids, social networks, etc., the lack of well-defined interaction weights hinders the ability to model the observed networked data using graph filters. Therefore, in this paper, we focus on the joint identification of coefficients and graph weights defining the graph filter that best models the observed input/output network data. While these two problems have been mostly addressed separately, we here propose an iterative method that exploits the knowledge of the support of the graph for the joint identification of graph filter coefficients and edge weights. We further show that our iterative scheme guarantees a non-increasing cost at every iteration, ensuring a globally-convergent behavior. Numerical experiments confirm the applicability of our proposed approach.
Original languageEnglish
Title of host publication2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)978-1-7281-6662-9
ISBN (Print)978-1-7281-6663-6
DOIs
Publication statusPublished - 2020
Event2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP) - Espoo, Finland
Duration: 21 Sep 202024 Sep 2020
Conference number: 33th

Workshop

Workshop2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)
CountryFinland
CityEspoo
Period21/09/2024/09/20

Keywords

  • Filtering over graphs
  • Graph filter identification
  • Graph signal processing
  • Networked data modeling
  • Topology identification

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