kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Gaussian Processes over Graphs
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0003-1285-8947
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0003-2638-6047
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-2718-0262
2020 (English)In: 2020 IEEE International Conference on Acoustics Speech and Signal Processing ICASSP, Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 5640-5644, article id 9053859Conference paper, Published paper (Refereed)
Abstract [en]

Kernel Regression over Graphs (KRG) was recently proposed for predicting graph signals in a supervised learning setting, where the inputs are agnostic to the graph. KRG model predicts targets that are smooth graph signals as over the given graph, given the input when all the signals are deterministic. In this work, we consider the development of a stochastic or Bayesian variant of KRG. Using priors and likelihood functions, our goal is to systematically derive a predictive distribution for the smooth graph signal target given the training data and a new input. We show that this naturally results in a Gaussian process formulation which we call Gaussian Processes over Graphs (GPG). Experiments with real-world datasets show that the performance of GPG is superior to a conventional Gaussian Process (without the graph-structure) for small training data sizes and under noisy training.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020. p. 5640-5644, article id 9053859
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keywords [en]
Graph signal processing, Gaussian processes, Bayesian estimation, kernel regression, graph-Laplacian
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-292363DOI: 10.1109/ICASSP40776.2020.9053859ISI: 000615970405180Scopus ID: 2-s2.0-85089226492OAI: oai:DiVA.org:kth-292363DiVA, id: diva2:1541524
Conference
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020; Barcelona; Spain; 4 May 2020 through 8 May 2020
Note

QC 20210401

Available from: 2021-04-01 Created: 2021-04-01 Last updated: 2022-06-25Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Venkitaraman, ArunChatterjee, SaikatHändel, Peter

Search in DiVA

By author/editor
Venkitaraman, ArunChatterjee, SaikatHändel, Peter
By organisation
Decision and Control Systems (Automatic Control)
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 114 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf