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» Sensing Matrix Optimization for Block-Sparse Decoding
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CORR
2010
Springer
225views Education» more  CORR 2010»
13 years 4 months ago
Sensing Matrix Optimization for Block-Sparse Decoding
Recent work has demonstrated that using a carefully designed sensing matrix rather than a random one, can improve the performance of compressed sensing. In particular, a welldesign...
Kevin Rosenblum, Lihi Zelnik-Manor, Yonina C. Elda...
CORR
2010
Springer
95views Education» more  CORR 2010»
13 years 4 months ago
Statistical Compressive Sensing of Gaussian Mixture Models
A new framework of compressive sensing (CS), namely statistical compressive sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical dist...
Guoshen Yu, Guillermo Sapiro
CORR
2008
Springer
234views Education» more  CORR 2008»
13 years 4 months ago
Bayesian Compressive Sensing via Belief Propagation
Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-N...
Dror Baron, Shriram Sarvotham, Richard G. Baraniuk
CORR
2011
Springer
210views Education» more  CORR 2011»
12 years 11 months ago
Statistical Compressed Sensing of Gaussian Mixture Models
A novel framework of compressed sensing, namely statistical compressed sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distribu...
Guoshen Yu, Guillermo Sapiro
CORR
2008
Springer
98views Education» more  CORR 2008»
13 years 4 months ago
Sparse Recovery by Non-convex Optimization -- Instance Optimality
In this note, we address the theoretical properties of p, a class of compressed sensing decoders that rely on p minimization with p (0, 1) to recover estimates of sparse and compr...
Rayan Saab, Özgür Yilmaz