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:84 - 12 self

Active Bibliography

Certified by............................................................................. – - 2009
Combining geometry and combinatorics: a unified approach to sparse signal
9 Near-Optimal Sparse Recovery in the L1 norm
43 Sparse recovery using sparse random matrices – - 2008
7 Sparse recovery using sparse matrices – - 2008
5 Topics in compressed sensing – - 2009
Compressive sensing: a paradigm shift in signal processing – - 812
Electronic Colloquium on Computational Complexity, Report No. 102 (2008) Finding Significant Fourier Transform Coefficients Deterministically and Locally – - 2008
3 Random observations on random observations: Sparse signal acquisition and processing – - 2010
34 Explicit constructions for compressed sensing of sparse signals – - 2008
Combinatorial Regression and Improved Basis Pursuit for Sparse Estimation – - 2012
1 Finding Significant Fourier Transform Coefficients Deterministically and Locally – - 2008
Compressive Sensing for Sparse Approximations: Constructions, Algorithms, and Analysis – - 2010
30 1 Sparse Recovery Using Sparse Matrices
24 Compressive Sensing – - 2010
16 Combinatorial sublinear-time fourier algorithms,” Submitted. Available at http://www.ima.umn.edu/∼iwen/index.html – - 2008
3 Sublinear time, measurement-optimal, sparse recovery for all – - 2010
ON THE DESIGN OF DETERMINISTIC MATRICES FOR FAST RECOVERY OF FOURIER COMPRESSIBLE FUNCTIONS
11 Construction of a Large Class of Deterministic Sensing Matrices that Satisfy a Statistical Isometry Property