Abstract
Generally in distributed signal processing, and specifically in distributed graph filters, reducing the communication and computational complexity plays a key role in the network lifetime. In this work we present a novel algorithm to sparsify the graph filtering operation in a random way, where each node decides locally with a certain probability with which of its neighbors to communicate. We show that, if the filter coefficients are changed accordingly, the first and second order moment of the stochastic output are identical to the deterministic filter output and bounded, respectively. We apply our idea on the tasks of signal denoising and diffusion. Numerical results show that the distributed implementation costs of the filter can be reduced up to 95% with a variance of 10-3 from the deterministic output.
Original language | English |
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Title of host publication | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
Pages | 5865-5869 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-5090-4117-6 |
DOIs | |
Publication status | Published - 2017 |
Event | ICASSP 2017: 42nd IEEE International Conference on Acoustics, Speech and Signal Processing - The Internet of Signals - Hilton New Orleans Riverside, New Orleans, LA, United States Duration: 5 Mar 2017 → 9 Mar 2017 Conference number: 42 http://www.ieee-icassp2017.org/ |
Conference
Conference | ICASSP 2017 |
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Abbreviated title | ICASSP |
Country/Territory | United States |
City | New Orleans, LA |
Period | 5/03/17 → 9/03/17 |
Internet address |
Keywords
- diffusion graph signals
- graph filters
- graph signal denoising
- graph signal processing
- graph sparsification