COMBINING GEOMETRY AND COMBINATORICS: A UNIFIED APPROACH TO SPARSE SIGNAL RECOVERY

by R. Berinde , A. C. Gilbert , P. Indyk , H. Karloff , M. J. Strauss
Citations:42 - 11 self

Active Bibliography

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21 Sparse recovery using sparse random matrices – Radu Berinde, Piotr Indyk - 2008
5 Sparse recovery using sparse matrices – Radu Berinde, Piotr Indyk, Radu Berinde, Piotr Indyk - 2008
1 Near-Optimal Sparse Recovery in the L1 norm – Piotr Indyk
183 CoSaMP: Iterative signal recovery from incomplete and inaccurate samples – D. Needell, J. A. Tropp - 2008
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24 Explicit constructions for compressed sensing of sparse signals – Piotr Indyk - 2008
Approved: – Deanna Needell, Roman Vershynin, Roman Vershynin (co-chair, Thomas Strohmer, Thomas Strohmer (co-chair, Jesús Deloera, Jesús Deloera - 905
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16 1 Sparse Recovery Using Sparse Matrices – Anna Gilbert, Piotr Indyk
4 Compressive Sensing – Massimo Fornasier, Holger Rauhut - 2010
7 Combinatorial sublinear-time fourier algorithms,” Submitted. Available at http://www.ima.umn.edu/∼iwen/index.html – M. A. Iwen - 2008
K-Median Clustering, Model-Based Compressive Sensing, and Sparse Recovery for Earth Mover Distance ∗ – Piotr Indyk, Eric Price - 2011
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Efficient Sparse Approximation Methods for Medical Imaging – Ray Maleh - 2009
28 Iteratively reweighted algorithms for compressive sensing – Rick Chartrand, Wotao Yin - 2008
Adaptive Compressed Image Sensing Using Dictionaries – Amir Averbuch, Shai Dekel, Shay Deutsch
7 Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization – Julio Martin Duarte-carvajalino, Guillermo Sapiro, Julio Martin Duarte-carvajalino, Guillermo Sapiro - 2008