Name
Abstract | ||
|---|---|---|
We consider the problem of embedding a low-dimensional set, M, from an infinite-dimensional Hilbert space, H, to a finite-dimensional space. Defining appropriate random linear projections, we propose two constructions of linear maps that have the restricted isometry property (RIP) on the secant set of M with high probability. The first one is optimal in the sense that it only needs a number of projections essentially proportional to the intrinsic dimension of M to satisfy the RIP. The second one, which is based on a variable density sampling technique, is computationally more efficient, while potentially requiring more measurements. |
Year | Venue | Keywords |
|---|---|---|
2015 | European Signal Processing Conference | Compressed sensing,restricted isometry property,box-counting dimension,variable density sampling |
DocType | ISSN | Citations |
Conference | 2076-1465 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gilles Puy | 1 | 106 | 13.82 |
| Mike E. Davies | 2 | 1664 | 120.39 |
| Rémi Gribonval | 3 | 1207 | 83.59 |