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» Sparse Recovery Using Sparse Random Matrices
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ICIP
2008
IEEE
16 years 3 months ago
Spatio-spectral reconstruction of the multispectral datacube using sparse recovery
Multispectral scene information is useful for radiometric graphics, material identification and imaging systems simulation. The multispectral scene can be described as a datacube,...
Manu Parmar, Steven Lansel, Brian A. Wandell
CPHYSICS
2007
222views more  CPHYSICS 2007»
15 years 2 months ago
JADAMILU: a software code for computing selected eigenvalues of large sparse symmetric matrices
A new software code for computing selected eigenvalues and associated eigenvectors of a real symmetric matrix is described. The eigenvalues are either the smallest or those closes...
Matthias Bollhöfer, Yvan Notay
NIPS
2008
15 years 3 months ago
Resolution Limits of Sparse Coding in High Dimensions
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...
Alyson K. Fletcher, Sundeep Rangan, Vivek K. Goyal
CORR
2010
Springer
114views Education» more  CORR 2010»
15 years 2 months ago
Sequential Compressed Sensing
Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of random measurements. Existing results in compressed sens...
Dmitry M. Malioutov, Sujay Sanghavi, Alan S. Wills...
SIAMSC
2008
132views more  SIAMSC 2008»
15 years 2 months ago
Stochastic Preconditioning for Diagonally Dominant Matrices
Abstract. This paper presents a new stochastic preconditioning approach for large sparse matrices. For the class of matrices that are row-wise and column-wise irreducibly diagonall...
Haifeng Qian, Sachin S. Sapatnekar