Publications associées à Compressive Sampling Strategies for Multichannel Signals

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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

Compressive Sampling Strategies for Multichannel Signals

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Chattez avec Graph Search

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing

Method, apparatus and computer program product for determining the location of a plurality of speech sources

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

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

Deliverable 3.1: Report on Learning Structured Sparse Models

Compressive sensing meets game theory

Structured Sparsity Models for Compressively Sensed Electrocardiogram Signals: A Comparative Study

Sparsity Driven People Localization with a Heterogeneous Network of Cameras

Sensing and Recovery under Sparsity Constraints

Sensing and Recovery under Sparsity Constraints

The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Structured Sparsity Models for Compressively Sensed Electrocardiogram Signals: A Comparative Study

Method, apparatus and computer program product for determining the location of a plurality of speech sources

Deliverable 3.1: Report on Learning Structured Sparse Models

Compressive sensing meets game theory

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

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

Sparsity Driven People Localization with a Heterogeneous Network of Cameras