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CORR
2010
Springer
143views Education» more  CORR 2010»
14 years 7 months ago
CUR from a Sparse Optimization Viewpoint
The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span o...
Jacob Bien, Ya Xu, Michael W. Mahoney
CORR
2010
Springer
145views Education» more  CORR 2010»
14 years 7 months ago
Orthogonal symmetric Toeplitz matrices for compressed sensing: Statistical isometry property
Recently, the statistical restricted isometry property (RIP) has been formulated to analyze the performance of deterministic sampling matrices for compressed sensing. In this paper...
Kezhi Li, Lu Gan, Cong Ling
JDCTA
2010
188views more  JDCTA 2010»
14 years 4 months ago
Compressive Sensing Signal Detection Algorithm Based on Location Information of Sparse Coefficients
Without reconstructing the signal themselves, signal detection could be solved by detection algorithm, which directly processes sampling value obtained from compressive sensing si...
Bing Liu, Ping Fu, Shengwei Meng, Lunping Guo
ICASSP
2011
IEEE
14 years 1 months ago
Deterministic compressed-sensing matrices: Where Toeplitz meets Golay
Recently, the statistical restricted isometry property (STRIP) has been formulated to analyze the performance of deterministic sampling matrices for compressed sensing. In this pa...
Kezhi Li, Cong Ling, Lu Gan
ICASSP
2011
IEEE
14 years 1 months ago
Improved model-based spectral compressive sensing via nested least squares
This paper introduces a new algorithm for reconstructing signals with sparse spectrums from noisy compressive measurements. The proposed model-based algorithm takes the signal str...
Mahdi Shaghaghi, Sergiy A. Vorobyov