6,744 research outputs found
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
This paper demonstrates theoretically and empirically that a greedy algorithm called Orthogonal Matching Pursuit (OMP) can reliably recover a signal with nonzero entries in dimension given random linear measurements of that signal. This is a massive improvement over previous results, which require measurements. The new results for OMP are comparable with recent results for another approach called Basis Pursuit (BP). In some settings, the OMP algorithm is faster and easier to implement, so it is an attractive alternative to BP for signal recovery problems
Relaxed Recovery Conditions for OMP/OLS by Exploiting both Coherence and Decay
We propose extended coherence-based conditions for exact sparse support
recovery using orthogonal matching pursuit (OMP) and orthogonal least squares
(OLS). Unlike standard uniform guarantees, we embed some information about the
decay of the sparse vector coefficients in our conditions. As a result, the
standard condition (where denotes the mutual coherence and
the sparsity level) can be weakened as soon as the non-zero coefficients
obey some decay, both in the noiseless and the bounded-noise scenarios.
Furthermore, the resulting condition is approaching for strongly
decaying sparse signals. Finally, in the noiseless setting, we prove that the
proposed conditions, in particular the bound , are the tightest
achievable guarantees based on mutual coherence
Recovery of Sparse Signals Using Multiple Orthogonal Least Squares
We study the problem of recovering sparse signals from compressed linear
measurements. This problem, often referred to as sparse recovery or sparse
reconstruction, has generated a great deal of interest in recent years. To
recover the sparse signals, we propose a new method called multiple orthogonal
least squares (MOLS), which extends the well-known orthogonal least squares
(OLS) algorithm by allowing multiple indices to be chosen per iteration.
Owing to inclusion of multiple support indices in each selection, the MOLS
algorithm converges in much fewer iterations and improves the computational
efficiency over the conventional OLS algorithm. Theoretical analysis shows that
MOLS () performs exact recovery of all -sparse signals within
iterations if the measurement matrix satisfies the restricted isometry property
(RIP) with isometry constant The recovery performance of MOLS in the noisy scenario is also
studied. It is shown that stable recovery of sparse signals can be achieved
with the MOLS algorithm when the signal-to-noise ratio (SNR) scales linearly
with the sparsity level of input signals
Coherence-based Partial Exact Recovery Condition for OMP/OLS
We address the exact recovery of the support of a k-sparse vector with
Orthogonal Matching Pursuit (OMP) and Orthogonal Least Squares (OLS) in a
noiseless setting. We consider the scenario where OMP/OLS have selected good
atoms during the first l iterations (l<k) and derive a new sufficient and
worst-case necessary condition for their success in k steps. Our result is
based on the coherence \mu of the dictionary and relaxes Tropp's well-known
condition \mu<1/(2k-1) to the case where OMP/OLS have a partial knowledge of
the support
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