Abstract
Graph sampling strategies require the signal to be relatively sparse in an alternative domain, e.g. bandlimitedness for reconstructing the signal. When such a condition is violated or its approximation demands a large bandwidth, the reconstruction often comes with unsatisfactory results even with large samples. In this paper, we propose an alternative sampling strategy based on a type of overcomplete graph-based dictionary. The dictionary is built from graph filters and has demonstrated excellent sparse representations for graph signals. We recognize the proposed sampling problem as a coupling between support recovery of sparse signals and node selection. Thus, to approach the problem we propose a sampling procedure that alternates between these two. The former estimates the sparse support via orthogonal matching pursuit (OMP), which in turn enables the latter to build the sampling set selection through greedy algorithms. Numerical results corroborate the role of key parameters and the effectiveness of the proposed method.
Original language | English |
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Title of host publication | 2021 29th European Signal Processing Conference (EUSIPCO) |
Subtitle of host publication | Proceedings |
Publisher | IEEE |
Pages | 1815-1819 |
Number of pages | 5 |
ISBN (Electronic) | 978-9-0827-9706-0 |
ISBN (Print) | 978-1-6654-0900-1 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 29th European Signal Processing Conference (EUSIPCO) - Virtual at Dublin, Ireland Duration: 23 Aug 2021 → 27 Aug 2021 Conference number: 29th |
Conference
Conference | 2021 29th European Signal Processing Conference (EUSIPCO) |
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Abbreviated title | EUSIPCO 2021 |
Country/Territory | Ireland |
City | Virtual at Dublin |
Period | 23/08/21 → 27/08/21 |
Bibliographical note
Accepted author manuscriptKeywords
- Compressive sensing
- Graph signal processing
- Graph signal sampling
- Signal reconstruction
- Sparse sensing