Graph Filters for Signal Processing and Machine Learning on Graphs

Elvin Isufi, Fernando Gama, David I. Shuman, Santiago Segarra

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks. Increasingly, modern data also reside on networks and other irregular domains whose structure is better captured by a graph. To process and learn from such data, graph filters account for the structure of the underlying data domain. In this article, we provide a comprehensive overview of graph filters, including the different filtering categories, design strategies for each type, and trade-offs between different types of graph filters. We discuss how to extend graph filters into filter banks and graph neural networks to enhance the representational power; that is, to model a broader variety of signal classes, data patterns, and relationships. We also showcase the fundamental role of graph filters in signal processing and machine learning applications. Our aim is that this article provides a unifying framework for both beginner and experienced researchers, as well as a common understanding that promotes collaborations at the intersections of signal processing, machine learning, and application domains.

Original languageEnglish
Pages (from-to)4745-4781
Number of pages37
JournalIEEE Transactions on Signal Processing
Volume72
DOIs
Publication statusPublished - 2024

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

  • collaborative filtering
  • Convolution
  • distributed processing
  • Filter banks
  • filter identification
  • Filtering theory
  • graph convolution
  • graph filter banks and wavelets
  • graph Gaussian processes
  • graph machine learning
  • graph neural networks
  • Graph signal processing
  • graph-based image processing
  • Information filters
  • Low-pass filters
  • Machine learning
  • matrix completion
  • mesh processing
  • point clouds
  • Signal processing
  • spectral clustering
  • topology identification

Fingerprint

Dive into the research topics of 'Graph Filters for Signal Processing and Machine Learning on Graphs'. Together they form a unique fingerprint.

Cite this