Perturbation Analysis of Orthogonal Matching Pursuit
نویسندگان
چکیده
منابع مشابه
Improved RIP Analysis of Orthogonal Matching Pursuit
Orthogonal Matching Pursuit (OMP) has long been considered a powerful heuristic for attacking compressive sensing problems; however, its theoretical development is, unfortunately, somewhat lacking. This paper presents an improved Restricted Isometry Property (RIP) based performance guarantee for -sparse signal reconstruction that asymptotically approaches the conjectured lower bound given in Da...
متن کاملTuning Free Orthogonal Matching Pursuit
Orthogonal matching pursuit (OMP) is a widely used compressive sensing (CS) algorithm for recovering sparse signals in noisy linear regression models. The performance of OMP depends on its stopping criteria (SC). SC for OMP discussed in literature typically assumes knowledge of either the sparsity of the signal to be estimated k0 or noise variance σ , both of which are unavailable in many pract...
متن کاملOrthogonal Matching Pursuit with Replacement
In this paper, we consider the problem of compressed sensing where the goal is to recover all sparsevectors using a small number of fixed linear measurements. For this problem, we propose a novelpartial hard-thresholding operator that leads to a general family of iterative algorithms. While oneextreme of the family yields well known hard thresholding algorithms like ITI and HTP[17, ...
متن کاملSimultaneous Orthogonal Matching Pursuit With Noise Stabilization: Theoretical Analysis
This paper studies the joint support recovery of similar sparse vectors on the basis of a limited number of noisy linear measurements, i.e., in a multiple measurement vector (MMV) model. The additive noise signals on each measurement vector are assumed to be Gaussian and to exhibit different variances. The simultaneous orthogonal matching pursuit (SOMP) algorithm is generalized to weight the im...
متن کاملOrthogonal Matching Pursuit from Noisy Measurements: A New Analysis∗
A well-known analysis of Tropp and Gilbert shows that orthogonal matching pursuit (OMP) can recover a k-sparse n-dimensional real vector from m = 4k log(n) noise-free linear measurements obtained through a random Gaussian measurement matrix with a probability that approaches one as n → ∞. This work strengthens this result by showing that a lower number of measurements, m = 2k log(n − k), is in ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2013
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2012.2222377