441 search results - page 39 / 89 » Sparse Recovery Using Sparse Random Matrices |
ICASSP
2011
IEEE
14 years 4 months ago
2011
IEEE
Sparse representations over redundant dictionaries offer an efficient paradigm for signal representation. Recently block-sparsity has been put forward as a prior condition for so...
121
Voted
UAI
2008
15 years 1 months ago
2008
Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our a...
100
Voted
CORR
2010
Springer
14 years 11 months ago
2010
Springer
We consider the problem of learning a coefficient vector x0 ∈ RN from noisy linear observation y = Ax0 + w ∈ Rn . In many contexts (ranging from model selection to image proce...
134
Voted
IEEECGIV
2009
IEEE
15 years 7 months ago
2009
IEEE
The theory of compressive sampling involves making random linear projections of a signal. Provided signal is sparse in some basis, small number of such measurements preserves the ...
103
Voted
ICASSP
2008
IEEE
15 years 7 months ago
2008
IEEE
The emerging theory of compressed sensing (CS) provides a universal signal detection approach for sparse signals at sub-Nyquist sampling rates. A small number of random projection...