Name
Abstract | ||
|---|---|---|
This letter provides tight upper bounds on the weak restricted isometry constant for compressed sensing with finite Gaussian measurement matrices. The bounds are used to develop a unified framework for the guaranteed recovery assessment of jointly sparse matrices from multiple measurement vectors. The analysis is based on the exact distribution of the extreme singular values of Gaussian matrices. ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LSP.2017.2729022 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Sparse matrices,Eigenvalues and eigenfunctions,Linear matrix inequalities,Minimization,Signal processing algorithms,Symmetric matrices,Upper bound | Mathematical optimization,Matrix analysis,Singular value,Matrix (mathematics),Upper and lower bounds,Gaussian,Mathematics,Sparse matrix,Restricted isometry property,Compressed sensing | Journal |
Volume | Issue | ISSN |
24 | 10 | 1070-9908 |
Citations | PageRank | References |
1 | 0.36 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed Elzanaty | 1 | 38 | 5.72 |
| Andrea Giorgetti | 2 | 110 | 10.93 |
| Marco Chiani | 3 | 1869 | 134.93 |