Node varying regularization for graph signals

Maosheng Yang, M. Coutino, E. Isufi, G. Leus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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While regularization on graphs has been successful for signal reconstruction, strategies for controlling the bias-variance trade-off of such methods have not been completely explored. In this work, we put forth a node varying regularizer for graph signal reconstruction and develop a minmax approach to design the vector of regularization parameters. The proposed design only requires as prior information an upper bound on the underlying signal energy; a reasonable assumption in practice. With such formulation, an iterative method is introduced to obtain a solution meeting global equilibrium. The approach is numerically efficient and has convergence guarantees. Numerical simulations using real data support the proposed design scheme.
Original languageEnglish
Title of host publication28th European Signal Processing Conference (EUSIPCO 2020)
Place of PublicationAmsterdam (Netherlands)
Number of pages5
ISBN (Electronic)978-9-0827-9705-3
Publication statusPublished - 2020
EventEUSIPCO 2020: The 28th European Signal Processing Conference - Amsterdam, Netherlands
Duration: 18 Jan 202122 Jan 2021
Conference number: 28th


ConferenceEUSIPCO 2020
OtherDate change due to COVID-19 (former date August 24-28 2020)


  • graph signal processing
  • bias-variance trade-off
  • minmax problem
  • graph signal denoising
  • graph regularization


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