Sampling and Reconstruction of Signals on Product Graphs

Guillermo Ortiz-Jimenez, Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

14 Citations (Scopus)
107 Downloads (Pure)

Abstract

In this paper, we consider the problem of subsampling and reconstruction of signals that reside on the vertices of a product graph, such as sensor network time series, genomic signals, or product ratings in a social network. Specifically, we leverage the product structure of the underlying domain and sample nodes from the graph factors. The proposed scheme is particularly useful for processing signals on large-scale product graphs. The sampling sets are designed using a low-complexity greedy algorithm and can be proven to be near-optimal. To illustrate the developed theory, numerical experiments based on real datasets are provided for sampling 3D dynamic point clouds and for active learning in recommender systems.

Original languageEnglish
Title of host publication2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Subtitle of host publicationProceedings
EditorsShuguang Cui, Hamid Jafarkhani
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages713-717
Number of pages5
ISBN (Electronic)978-1-7281-1295-4
ISBN (Print)978-1-7281-1296-1
DOIs
Publication statusPublished - 2018
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: 26 Nov 201829 Nov 2018

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
Country/TerritoryUnited States
CityAnaheim
Period26/11/1829/11/18

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Active learning
  • Graph signal processing
  • Product graphs
  • Sparse sampling
  • Submodularity

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