Name
Abstract | ||
|---|---|---|
Adaptive sampling theory has shown that, with proper assumptions on the signal class, algorithms exist to reconstruct a signal in $\mathbb {R}^d$ with an optimal number of samples. We generalize this problem to the case of spatial signals, where the sampling cost is a function of both the number of samples taken and the distance traveled during estimation. This is motivated by our work studying re... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TSP.2017.2731323 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Signal processing algorithms,Lakes,Noise measurement,Algorithm design and analysis,Heuristic algorithms,Estimation error,Sea measurements | Active learning,Adaptive sampling,Quantile,Sampling (statistics),Artificial intelligence,Binary search algorithm,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
65 | 20 | 1053-587X |
Citations | PageRank | References |
1 | 0.37 | 18 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Lipor | 1 | 18 | 3.53 |
| Brandon P. Wong | 2 | 1 | 0.71 |
| Don Scavia | 3 | 2 | 1.40 |
| Branko Kerkez | 4 | 124 | 11.53 |
| Laura Balzano | 5 | 410 | 27.51 |