Name
Abstract | ||
|---|---|---|
This paper considers the graph signal sampling problem when some of the selected samples are lost or unavailable due to sensor failures or adversarial erasures. We formulate a robust graph signal sampling problem where only a subset of selected samples are received, and the goal is to maximize the worst-case performance. We propose a novel greedy robust sample selection algorithm and study its performance guarantees. Our numerical results demonstrate the performance improvement of the proposed algorithm over the existing schemes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8682340 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Graph signal sampling, robust optimization, approximate submodular function maximization | Approximation algorithm,Graph,Noise measurement,Pattern recognition,Computer science,Algorithm,Greedy algorithm,Artificial intelligence,Sampling (statistics),Sample selection,Signal processing algorithms,Performance improvement | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Basak Guler | 1 | 2 | 2.06 |
| Ajinkya Jayawant | 2 | 0 | 0.68 |
| Amir Salman Avestimehr | 3 | 1880 | 157.39 |
| Antonio Ortega | 4 | 4720 | 493.26 |