In matrix completion under noisy measurements, most available results assume that there is an a priori bound on the Frobenius norm of noise, and derive bounds residual error. this letter, we obtain “component-wise” error, based a similar noise. This among first such results. As in our earlier p...

We investigate the robust PCA problem of decomposing an observed matrix into the sum of a low-rank and a sparse error matrices via convex programming Principal Component Pursuit (PCP). In contrast to previous studies that assume the support of the error matrix is generated by uniform Bernoulli sampling, we allow non-uniform sampling, i.e., entries of the low-rank matrix are corrupted by errors ...

Estimation of a high dimensional precision matrix is critical problem to many areas statistics including Gaussian graphical models and inference on data. Working under the structural assumption sparsity, we propose novel methodology for estimating such matrices while controlling false positive rate, percentage entries incorrectly chosen be non-zero. We specifically focus rates tending towards z...

This paper investigates an error covariance matrix splitting technique for multiuser multiple input and multiple output (MIMO) interference downlink channel. Most of the related work has thus far considered the traditional error covariance matrix which has not been well-shaped for maximizing the system capacity. Thus, we split and propose a new iterative error covariance matrix to mitigate the ...

  Background: Human error is the most important cause of occupational and non-occupational accidents. Because, it seems necessary to identify, predict and analyze human errors, and also offer appropriate control strategies to reduce errors which cause adverse consequences, the present study was carried out with the aim of identifying human errors while operating meat grinder and offer sugg...