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Graph Signal Processing: Overview, Challenges, and Applications

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Other literature type 01 May 2018Embargo end date: 01 Jan 2017Publisher:Institute of Electrical and Electronics Engineers (IEEE)Journal:Proceedings of the IEEE, volume 106, pages 808-828 (issn: 0018-9219, eissn: 1558-2256, Copyright policy )
Authors: Antonio Ortega; Pascal Frossard; Jelena Kovacevic; José M. F. Moura; Pierre Vandergheynst;

doi: 10.1109/jproc.2018.2820126 , 10.48550/arxiv.1712.00468

arXiv: 1712.00468

Graph Signal Processing: Overview, Challenges, and Applications

Abstract

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.

To appear, Proceedings of the IEEE

Related Organizations
Keywords

Signal Processing (eess.SP), FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1K
Top 0.01%
Top 0.1%
Top 0.01%
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bronze