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We propose to solve the covariance estimator via a simple iterative shrinkage-thresholding algorithm (C-ISTA) with provable convergence.
In this paper, a proximal gradient method (G-ISTA) for performing `1-regularized covariance matrix estimation is presented. Although numerous algorithms have.
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We propose to solve the covariance estimator via a simple iterative shrinkage-thresholding algorithm (C-ISTA) with provable convergence. Numerical simulations ...
C-ISTA can also be used to estimate the correlation matrices by rescaling the shrinkage parameters for each entry of the penalty. Simulations with ...
We propose and analyze a new estimator of the covariance matrix that admits strong theoretical guarantees under weak assumptions on the underlying distribution, ...
Jul 2, 2023 · C-ISTA: Iterative Shrinkage-Thresholding Algorithm for Sparse Covariance Matrix Estimation. Wenfu Xia 1. ,. Ziping Zhao 1.
Abstract. We consider the class of iterative shrinkage-thresholding algorithms (ISTA) for solving linear inverse problems arising in signal/image processing.
In this paper, a proximal gradient method (G-ISTA) for performing L1-regularized covariance matrix estimation is presented. Although numerous algorithms ...
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Nov 12, 2012 · In this paper, a proximal gradient method (G-ISTA) for performing L1-regularized covariance matrix estimation is presented.
Missing: C- Shrinkage-