Name
Abstract | ||
|---|---|---|
In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into expectation-maximization-based sparse Bayesian learning (SBL). In particular, GGAMP is used to implement the E-step in SBL in place of matrix inversion, leveraging the fact that GGAMP is guaranteed to converge with appropriate damping.... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TSP.2017.2764855 | IEEE Transactions on Signal Processing |
Keywords | DocType | Volume |
Signal processing algorithms,Approximation algorithms,Bayes methods,Complexity theory,Sparse matrices,Robustness,Convergence | Journal | 66 |
Issue | ISSN | Citations |
2 | 1053-587X | 11 |
PageRank | References | Authors |
0.55 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maher Al-Shoukairi | 1 | 26 | 2.25 |
| Philip Schniter | 2 | 1620 | 93.74 |
| Bhaskar Rao | 3 | 4037 | 449.28 |