Kernel-based reconstruction of space-time functions via extended graphs

Vassilis N. Ioannidis, Daniel Romero, Georgios B. Giannakis

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

5 Scopus citations


Signals evolving over graphs emerge naturally in a number of applications related to network science. A frequently encountered challenge pertains to reconstructing such signals given their values on subsets of vertices at possibly different time instants. Spatiotemporal dynamics can be leveraged so that a small number of vertices suffices to achieve accurate reconstruction. The present paper broadens the existing kernel-based graph-function reconstruction framework to handle time-evolving functions over (possibly dynamic) graphs. The proposed approach introduces the novel notion of graph extension to enable kernel-based estimators over time and space. Numerical tests with real data corroborate that judiciously capturing time-space dynamics markedly improves reconstruction performance.

Original languageEnglish (US)
Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538639542
StatePublished - Mar 1 2017
Event50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States
Duration: Nov 6 2016Nov 9 2016

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
CountryUnited States
CityPacific Grove

Bibliographical note

Funding Information:
Work supported by grants ARO grant W911NF-15-1-0492 and NSF grants 1343248, 1442686, and 1514056


  • Graph signal reconstruction
  • graph extension
  • kernel ridge regression
  • space-time kernels

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