Node varying regularization for graph signals

Conference Paper (2020)
Author(s)

Maosheng Yang (TU Delft - Electrical Engineering, Mathematics and Computer Science)

M. Coutino (TU Delft - Electrical Engineering, Mathematics and Computer Science)

E. Isufi (TU Delft - Electrical Engineering, Mathematics and Computer Science)

G. Leus (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Research Group
Signal Processing Systems
DOI related publication
https://doi.org/10.23919/Eusipco47968.2020.9287807 Final published version
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Publication Year
2020
Language
English
Research Group
Signal Processing Systems
Article number
9287807
Pages (from-to)
845-849
ISBN (electronic)
978-9-0827-9705-3
Event
EUSIPCO 2020 (2021-01-18 - 2021-01-22), Amsterdam, Netherlands
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Abstract

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.

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