Graph Signal Processing: Overview, Challenges, and Applications

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, along with a brief historical perspective to highli...

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Veröffentlicht in:Proceedings of the IEEE Jg. 106; H. 5; S. 808 - 828
Hauptverfasser: Ortega, Antonio, Frossard, Pascal, Kovacevic, Jelena, Moura, Jose M. F., Vandergheynst, Pierre
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Data models
Data processing
Digital signal processing
Digital signal processors
Filtration
Graph signal processing (GSP)
Graph theory
Graphical models
Image processing
Laplace equations
Machine learning
network science and graphs
sampling
Sampling methods
Signal processing
Social network services
ISSN:0018-9219, 1558-2256
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Zusammenfassung: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, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas. We then summarize recent advances 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2018.2820126