Abstract
Graph smoothness is an important prior used for designing sampling strategies for graph signals as well as for regularizing the problem of graph learning. Additionally, smoothness is an appropriate assumption for graph signal processing (GSP) tasks such as filtering or signal recovery from samples. The most popular measure of smoothness is the quadratic form of the Laplacian, which naturally follows from the factor analysis approach. This paper presents a novel smoothness measure based on the graph correlation. The proposed measure enhances the applicability of graph smoothness measures across a variety of GSP tasks, by facilitating interoperability and generalizing across shift operators.
| Original language | English |
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| Title of host publication | 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings |
| Publisher | European Signal Processing Conference (EUSIPCO) |
| Pages | 1848-1852 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-9-4645-9360-0 |
| DOIs | |
| Publication status | Published - 2023 |
| MoE publication type | A4 Conference publication |
| Event | European Signal Processing Conference - Helsinki, Finland Duration: 4 Sept 2023 → 8 Sept 2023 Conference number: 31 https://eusipco2023.org/ |
Publication series
| Name | European Signal Processing Conference |
|---|---|
| ISSN (Print) | 2219-5491 |
Conference
| Conference | European Signal Processing Conference |
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| Abbreviated title | EUSIPCO |
| Country/Territory | Finland |
| City | Helsinki |
| Period | 04/09/2023 → 08/09/2023 |
| Internet address |
Keywords
- graph autocorrelation
- graph autocovariance
- Graph signal processing
- graph smoothness measures