Name
Title | ||
|---|---|---|
An empirical-bayes approach to recovering linearly constrained non-negative sparse signals. |
Abstract | ||
|---|---|---|
We propose two novel approaches for the recovery of an (approximately) sparse signal from noisy linear measurements in the case that the signal is a priori known to be non-negative and obey given linear equality constraints, such as a simplex signal. This problem arises in, e.g., hyperspectral imaging, portfolio optimization, density estimation, and certain cases of compressive imaging. Our first ... |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TSP.2014.2337841 | IEEE Transactions on Signal Processing |
Keywords | DocType | Volume |
Approximation algorithms,Signal processing algorithms,Approximation methods,Optimization,AWGN,Vectors | Journal | 62 |
Issue | ISSN | Citations |
18 | 1053-587X | 7 |
PageRank | References | Authors |
0.48 | 16 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeremy P. Vila | 1 | 106 | 4.38 |
| Philip Schniter | 2 | 1620 | 93.74 |