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.
| Comments: | To appear, Proceedings of the IEEE |
| Subjects: | Signal Processing (eess.SP) |
| Cite as: | arXiv:1712.00468 [eess.SP] |
| (or arXiv:1712.00468v2 [eess.SP] for this version) | |
| https://doi.org/10.48550/arXiv.1712.00468 |
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arXiv-issued DOI via DataCiteFrom: Antonio Ortega [view email]
[v1] Fri, 1 Dec 2017 19:14:22 UTC (3,310 KB)
[v2] Mon, 26 Mar 2018 16:46:32 UTC (4,690 KB)
View a PDF of the paper titled Graph Signal Processing: Overview, Challenges and Applications, by Antonio Ortega and 4 other authors