10 search results - page 1 / 2 » Nonuniform Sparse Recovery with Gaussian Matrices |
CORR
2010
Springer
13 years 4 months ago
2010
Springer
Compressive sensing predicts that sufficiently sparse vectors can be recovered from highly incomplete information. Efficient recovery methods such as 1-minimization find the sparse...
CORR
2008
Springer
13 years 4 months ago
2008
Springer
We study the information-theoretic limits of exactly recovering the support set of a sparse signal, using noisy projections defined by various classes of measurement matrices. Our ...
ICASSP
2011
IEEE
12 years 8 months ago
2011
IEEE
—We present an alternative analysis of weighted 1 minimization for sparse signals with a nonuniform sparsity model, and extend our results to nuclear norm minimization for matric...
CISS
2011
IEEE
12 years 8 months ago
2011
IEEE
—In compressive sensing (CS), the Restricted Isometry Property (RIP) is a powerful condition on measurement operators which ensures robust recovery of sparse vectors is possible ...
SIAMJO
2011
12 years 11 months ago
2011
Many applications arising in a variety of fields can be well illustrated by the task of recovering the low-rank and sparse components of a given matrix. Recently, it is discovered...