Name
Abstract | ||
|---|---|---|
This paper studies regularized least square recovery of signals whose samplesu0027 prior distributions are nonidentical, e.g., signals with time-variant sparsity. For this model, Bayesian framework suggests to regularize the least squares term with an asymmetric penalty. We investigate this problem in two respects: First, we characterize the asymptotic performance via the replica method and then discuss algorithmic approaches to the problem. Invoking the asymptotic characterization of the performance, we propose a tuning strategy to optimally tune the algorithmic approaches for recovery. To demonstrate applications of the results, the particular example of BPSK recovery is investigated and the efficiency of the proposed strategy is depicted in the shadow of results available in the literature |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Information Theory | Least squares,Replica,Shadow,Mathematical optimization,Mathematics,Bayesian probability,Phase-shift keying |
DocType | Volume | Citations |
Journal | abs/1805.11895 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Bereyhi | 1 | 37 | 14.09 |
| Mohammad Ali Sedaghat | 2 | 72 | 12.19 |
| R. Muller | 3 | 1206 | 124.92 |