Kernel-based inference of functions over graphs

Vassilis N. Ioannidis, Meng Ma, Athanasios N. Nikolakopoulos, Georgios B. Giannakis, Daniel Romero

Research output: Chapter in Book/Report/Conference proceedingChapter

18 Scopus citations

Abstract

The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced. A particularly interesting-and prevalent in several fields of study-problem is that of inferring a function defined over the nodes of a network. This work presents a versatile kernel-based framework for tackling this inference problem that naturally subsumes and generalizes the reconstruction approaches put forth recently for the signal processing by the community studying graphs. Both the static and the dynamic settings are considered along with effective modeling approaches for addressing real-world problems. The analytical discussion herein is complemented with a set of numerical examples, which showcase the effectiveness of the presented techniques, as well as their merits related to state-of-the-art methods.

Original languageEnglish (US)
Title of host publicationAdaptive Learning Methods for Nonlinear System Modeling
PublisherElsevier
Pages173-198
Number of pages26
ISBN (Electronic)9780128129760
ISBN (Print)9780128129777
DOIs
StatePublished - Jan 1 2018

Keywords

  • Dynamic graphs
  • Graph function reconstruction
  • Kernel kalman filter
  • Kernel-based learning
  • Signal processing on graphs

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