… observation of the compressed samples y x, U = r whose covariance matrix y R is given by
… Most of the methods described in this article make use of the sample covariance matrix of ,y …

… On the other hand, sparsity is the key attribute exploited by modern compressive sampling
… are developed to address the perturbed compressive sampling (and the related dictionary …

… To mitigate this problem, we impute the empty entries with random samples drawn iid
from a … an approximate covariance matrix ^66(19), from which we then compute the eigenvector. …

In this paper, we study the number of measurements required to recover a sparse signal in
C M with L nonzero coefficients from compressed samples in the presence of noise. We …

… applications of sparse representations and compressive sampling (CS) approaches in …
different forms and can correspond to missing or compressed data, or even refer generally to …

… samples are sufficient to approximate the ground truth [4], [30]. In contrast, this paper is
motivated by the success of Compressed … in this paper, covariance estimation from compressive …

… sampling there often are fully sampled k‐space dimensions. In this work, a new coil compression
technique for Cartesian sampling … dimensions for better compression and computation …

… covariance matrix in a sparse way, instead of searching for an optimal diagonal loading factor
for the sample covariance … can be estimated from a compressive sensing (CS) problem. In …

… covariance matrix , define the sample covariance matrices … sample covariances and and
to base the test on the maximum differences. It is important to note that the sample covariances ’…

… compressive properties of wavelets assure that is typically small for , thereby motivating the
use of wavelets in a new generation of compression … with compressed samples for each task, …