Publications associées à Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

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Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Volkan Cevher, Anastasios Kyrillidis

The least absolute shrinkage and selection operator (LASSO) for linear regression exploits the geometric interplay of the

\ell_2

-data error objective and the

\ell_1

-norm constraint to arbitrarily select sparse models. Guiding this uninformed selection ...

IEEE2012

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

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Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

Hard Thresholding with Norm Constraints

JOINT TRACE/TV NORM MINIMIZATION: A NEW EFFICIENT APPROACH FOR SPECTRAL COMPRESSIVE IMAGING

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Hyperspectral Image Compressed Sensing Via Low-Rank And Joint-Sparse Matrix Recovery

Fast Geodesic Active Fields for Image Registration based on Splitting and Augmented Lagrangian Approaches

Compressive sensing meets game theory

Guaranteed recovery of a low-rank and joint-sparse matrix from incomplete and noisy measurements

Multi-party Speech Recovery Exploiting Structured Sparsity Models

Multi-party Speech Recovery Exploiting Structured Sparsity Models

Hyperspectral Image Compressed Sensing Via Low-Rank And Joint-Sparse Matrix Recovery

Fast Geodesic Active Fields for Image Registration based on Splitting and Augmented Lagrangian Approaches

Hard Thresholding with Norm Constraints

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Guaranteed recovery of a low-rank and joint-sparse matrix from incomplete and noisy measurements

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

JOINT TRACE/TV NORM MINIMIZATION: A NEW EFFICIENT APPROACH FOR SPECTRAL COMPRESSIVE IMAGING

Multi-party Speech Recovery Exploiting Structured Sparsity Models

Compressive sensing meets game theory

Multi-party Speech Recovery Exploiting Structured Sparsity Models