Abstract
The main challenges distributed graph filters face in practice are the communication overhead and computational complexity. In this work, we extend the state-of-the-art distributed finite impulse response (FIR) graph filters to an edge-variant (EV) version, i.e., a filter where every node weights the signals from its neighbors with different values. Besides having the potential to reduce the filter order leading to amenable communication and complexity savings, the EV graph filter generalizes the class of classical and node-variant FIR graph filters. Numerical tests validate our findings and illustrate the potential of the EV graph filters to (i) approximate a user-provided frequency response; and (ii) implement distributed consensus with much lower orders than its direct contenders.
Original language | English |
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Title of host publication | 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
Pages | 1-5 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-5386-1251-4 |
ISBN (Print) | 978-1-5386-1252-1 |
DOIs | |
Publication status | Published - 2018 |
Event | 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Willemstad, Curaçao Duration: 10 Dec 2017 → 13 Dec 2017 Conference number: 7 http://www.cs.huji.ac.il/conferences/CAMSAP17/ |
Workshop
Workshop | 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing |
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Abbreviated title | CAMSAP |
Country/Territory | Curaçao |
City | Willemstad |
Period | 10/12/17 → 13/12/17 |
Internet address |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Edge-variant filters
- finite-time consensus
- FIR graph filters
- graph filters
- graph signal processing