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Sparse Solution of Underdetermined Linear Equations by Stagewise Orthogonal Matching Pursuit
compressed sensing decoding error-correcting codes
2015/8/21
Finding the sparsest solution to underdetermined systems of linear equations y = Φx is NP-hard
in general. We show here that for systems with ‘typical’/‘random’ Φ, a good approximation to the
sparse...
Coherence-Based Performance Guarantees of Orthogonal Matching Pursuit
Compressive Sensing(CS) Orthogonal Matching Pursuit(OMP) worst-case coherence average coherence support recovery signal reconstruction
2012/11/23
In this paper, we present coherence-based performance guarantees of Orthogonal Matching Pursuit (OMP) for both support recovery and signal reconstruction of sparse signals when the measurements are co...
Orthogonal Matching Pursuit with Noisy and Missing Data: Low and High Dimensional Results
Orthogonal Matching Pursuit Noisy and Missing Data High Dimensional Results Statistics Theory
2012/6/21
Many models for sparse regression typically assume that the covariates are known completely, and without noise. Particularly in high-dimensional applications, this is often not the case. This paper de...
In this paper, we consider the problem of compressed sensing where the goal is to recover almost all the sparse vectors using a small number of fixed linear measurements. For this problem, we propose ...
Improved RIP Analysis of Orthogonal Matching Pursuit
compressive sensing sparse approximation orthogonal matching pursuit restricted isometry property greedy algorithms error bounds
2011/3/25
Orthogonal Matching Pursuit (OMP) has long been considered a powerful heuristic for attacking compressive sensing problems; however, its theoretical development is, unfortunately, somewhat lacking. Th...
A* Orthogonal Matching Pursuit: Best-First Search for Compressed Sensing Signal Recovery
compressed sensing best-first search A* search matching pursuit sparse representations sparse signal
2010/11/29
Compressed sensing is a recently developing area which is interested in reconstruction of sparse signals acquired in reduced dimensions. Acquiring the data with a small number of samples makes the rec...