Signal and Graph Perturbations via Total Least-Squares

Elena Ceci, Yanning Shen, Georgios B. Giannakis, Sergio Barbarossa

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

2 Scopus citations


Graphs are pervasive in various applications capturing the complex behavior observed in biological, financial, and social networks, to name a few. Two major learning tasks over graphs are topology identification and inference of signals evolving over graphs. Existing approaches typically aim at identifying the topology when signals on all nodes are observed, or, recovering graph signals over networks with known topologies. In practice however, signal or graph perturbations can be present in both tasks, due to model mismatch, outliers, outages or adversaries. To cope with these perturbations, this work introduces regularized total least-squares (TLS) based approaches and corresponding alternating minimization algorithms with convergence guarantees. Tests on simulated data corroborate the effectiveness of the novel TLS-based approaches.

Original languageEnglish (US)
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538692189
StatePublished - Jul 2 2018
Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
Duration: Oct 28 2018Oct 31 2018

Publication series

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


Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Country/TerritoryUnited States
CityPacific Grove

Bibliographical note

Funding Information:
Work in this paper was supported by grants NSF 1711471, 1500713 and NIH 1R01GM104975-01.

Publisher Copyright:
© 2018 IEEE.


  • Graph and signal perturbations
  • graph signal reconstruction
  • structural equation models
  • topology identification
  • total leastsquares


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