TY - GEN
T1 - The continuous joint sparsity prior for sparse representations
T2 - 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
AU - Mishali, Moshe
AU - Eldar, Yonina C.
PY - 2007
Y1 - 2007
N2 - The classical problem discussed in the literature of compressed sensing is recovering a sparse vector from a relatively small number of linear non-adaptive projections. In this paper, we study the recovery of a continuous set of sparse vectors sharing a common set of locations of their non-zero entries. This model includes the classical sparse representation problem, and also its known extensions. We develop a method for joint recovery of the entire set of sparse vectors by the solution of just one finite dimensional problem. The proposed strategy is exact and does not use heuristics or discretization methods. We then apply our method to two applications: The first is spectrum-blind reconstruction of multi-band analog signals from point-wise samples at a sub-Nyquist rate. The second application is to the well studied multiple-measurement-vectors problem which addresses the recovery of a finite set of sparse vectors.
AB - The classical problem discussed in the literature of compressed sensing is recovering a sparse vector from a relatively small number of linear non-adaptive projections. In this paper, we study the recovery of a continuous set of sparse vectors sharing a common set of locations of their non-zero entries. This model includes the classical sparse representation problem, and also its known extensions. We develop a method for joint recovery of the entire set of sparse vectors by the solution of just one finite dimensional problem. The proposed strategy is exact and does not use heuristics or discretization methods. We then apply our method to two applications: The first is spectrum-blind reconstruction of multi-band analog signals from point-wise samples at a sub-Nyquist rate. The second application is to the well studied multiple-measurement-vectors problem which addresses the recovery of a finite set of sparse vectors.
KW - Joint sparsity prior
KW - Multiband sampling
KW - Multiple-measurement vector (MMV)
KW - Nonuniform periodic sampling
KW - Sparse representation
UR - http://www.scopus.com/inward/record.url?scp=50249127751&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP.2007.4497981
DO - 10.1109/CAMSAP.2007.4497981
M3 - Conference contribution
AN - SCOPUS:50249127751
SN - 9781424417148
T3 - 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
SP - 125
EP - 128
BT - 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
Y2 - 12 December 2007 through 14 December 2007
ER -