Robust Identification of Graph Structure

Georgios V. Karanikolas, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Partial correlations (PCs) and the related inverse covariance matrix adopted by graphical lasso, are widely applicable tools for learning graph connectivity given nodal observations. The resultant estimators however, can be sensitive to outliers. Robust approaches developed so far to cope with outliers do not (explicitly) account for nonlinear interactions possibly present among nodal processes. This can hurt the identification of graph connectivity, merely due to model mismatch. To overcome this limitation, a novel formulation of robust PC is introduced based on nonlinear kernel functions. The proposed scheme leverages robust ridge regression techniques, spectral Fourier feature based kernel approximants, and robust association measures. Numerical tests on synthetic and real data illustrate the potential of the novel approach.

Original languageEnglish (US)
Title of host publication2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages486-490
Number of pages5
ISBN (Electronic)9798350344523
DOIs
StatePublished - 2023
Externally publishedYes
Event9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023 - Herradura, Costa Rica
Duration: Dec 10 2023Dec 13 2023

Publication series

Name2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023

Conference

Conference9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
Country/TerritoryCosta Rica
CityHerradura
Period12/10/2312/13/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • kernel-based methods
  • network topology inference
  • Robust statistics

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