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The covariance matrix estimation in compound-Gaussian environment is generally intractable.
In this paper, we propose to apply OptShrink for low-rank matrix estimation in fMRI reconstruction using low-rank plus sparse decomposition.
After having discussed the three approaches for the scatter matrix estimation in t-distributed data, we can introduce the classical radar detection problem.
The paper by J. Lei et al. proposes a link gain Matrix estimation in distributed large-scale wireless networks, with the goal to devise a methodology that measures a fraction of the links and accurately estimates the rest.
The performance of the detector was evaluated in terms of ROC curves, and by objective evaluation of the extracted signals when the detector is applied for PSD matrix estimation in an informed MVDR filtering framework.
Given the impact of non-stationary clutter environments above, the traditional adaptive SAR/GMTI scheme based on the covariance matrix estimation in (2) is prone to be erroneous in heterogeneous clutter environments.
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Motivated by this issue, in this paper, we investigate the traffic matrix estimation problem in an IP-over-WDM backbone network.
Traffic matrix estimation consists in inferring a traffic matrix from link-level measurements.
Before passing to discuss the scatter matrix estimation problem in complex t-distributed data, few remarks are needed.
More specifically, Section "Main steps of standard UBSS methods" recalls the source recovery and mixing matrix estimation steps in classical UBSS methods based on sparseness assumptions.
In our detection schemes, we will use αapprox in (26), the approximate value of the shrinkage parameter in the covariance matrix estimation process indicated in (25).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com