JMLR
2012
11 years 7 months ago
2012
Standard compressive sensing results state that to exactly recover an s sparse signal in Rp , one requires O(s · log p) measurements. While this bound is extremely useful in prac...
TSP
2010
12 years 11 months ago
2010
We consider the problem of recursively and causally reconstructing time sequences of sparse signals (with unknown and time-varying sparsity patterns) from a limited number of noisy...
MP
2002
13 years 4 months ago
2002
In this paper we describe an automatic procedure for successively reducing the set of possible nonzeros in a Jacobian matrix until eventually the exact sparsity pattern is obtained...
CORR
2007
Springer
13 years 4 months ago
2007
Springer
The problem of recovering the sparsity pattern of a fixed but unknown vector β∗ ∈ Rp based on a set of n noisy observations arises in a variety of settings, including subset...
CORR
2010
Springer
13 years 4 months ago
2010
Springer
Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is performed by solving an 1-regularized linear regression prob...
ICASSP
2010
IEEE
13 years 4 months ago
2010
IEEE
In Cognitive Radio scenarios channelization information from primary network may be available to the spectral monitor. Under this assumption we propose a spectral estimation algor...
NIPS
2008
13 years 6 months ago
2008
This paper addresses the problem of sparsity pattern detection for unknown ksparse n-dimensional signals observed through m noisy, random linear measurements. Sparsity pattern rec...
ICIP
2008
IEEE
14 years 6 months ago
2008
IEEE
We consider the problem of reconstructing time sequences of spatially sparse signals (with unknown and time-varying sparsity patterns) from a limited number of linear "incohe...