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

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Model-based Sketching and Recovery with Expanders

Volkan Cevher, Luca Baldassarre, Bubacarr Bah

Linear sketching and recovery of sparse vectors with randomly constructed sparse matrices has numerous applications in several areas, including compressive sensing, data stream computing, graph sketching, and combinatorial group testing. This paper conside ...

2014

Jump-Sparse and Sparse Recovery Using Potts Functionals

Martin Kurt Storath

We recover jump-sparse and sparse signals from blurred incomplete data corrupted by (possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical results (existence of minimizers, complexity) on inverse Potts functionals and pr ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Fast and robust estimation of jointly sparse channels

Martin Vetterli, Ali Hormati, Yann Barbotin

A device and method for estimating multipath jointly sparse channels. The method comprises receiving a number K of signal components by a number P of receiving antennas, where P≧2. The method further comprises estimating the sparsity condition of the multi ...

2013

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

Pierre Vandergheynst, Mohammad Golbabaee

In this paper we consider the problem of recovering a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model that can efficiently restrict the degrees of freedom of the problem and is generic enough to ...

Institute of Electrical and Electronics Engineers2012

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

Pierre Vandergheynst, Mohammad Golbabaee

In this paper we consider recovery of a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model which can efficiently restricts the degrees of freedom of data and, at the same time, is generic so that f ...

2012

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

Pierre Vandergheynst, Mohammad Golbabaee

We propose a novel approach to reconstruct Hyperspectral images from very few number of noisy compressive measurements. Our reconstruction approach is based on a convex minimization which penalizes both the nuclear norm and the l(2,1) mixed-norm of the dat ...

Ieee2012

Multi-Way Compressed Sensing for Sparse Low-Rank Tensors

Anastasios Kyrillidis

For linear models, compressed sensing theory and methods enable recovery of sparse signals of interest from few measurements-in the order of the number of nonzero entries as opposed to the length of the signal of interest. Results of similar flavor have mo ...

Institute of Electrical and Electronics Engineers2012

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

Pierre Vandergheynst, Mohammad Golbabaee

We propose a novel approach to reconstruct Hyperspectral images from very few number of noisy compressive measure- ments. Our reconstruction approach is based on a convex minimiza- tion which penalizes both the nuclear norm and the l2,1 mixed-norm of the d ...

Ieee2012

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

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Compressed Sensing of Memoryless Sources

Model-based Sketching and Recovery with Expanders

Jump-Sparse and Sparse Recovery Using Potts Functionals

Fast and robust estimation of jointly sparse channels

Compressive Sampling Strategies for Multichannel Signals

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

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

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

Multi-Way Compressed Sensing for Sparse Low-Rank Tensors

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

Compressed Sensing of Memoryless Sources

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

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

Model-based Sketching and Recovery with Expanders

Compressive Sampling Strategies for Multichannel Signals

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

Jump-Sparse and Sparse Recovery Using Potts Functionals

Fast and robust estimation of jointly sparse channels

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

Multi-Way Compressed Sensing for Sparse Low-Rank Tensors