Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 22, Issue 11, Pages 2127-2131Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2015.2462724
Keywords
Covariance estimation; diagonal loading; empirical Bayes; minimum mean square error (MMSE); regularization; robust estimation; shrinkage
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An Empirical Bayes formalization of the regularized covariance estimation problem is proposed for (possibly high-dimensional, low-sample) normal variates. A simple iteration is provided to automatically adjust the shrinkage level, which provably converges to the maximum likelihood hyperparameter estimation for any choice of the starting point. The proposed approach is effective and can outperform both MSE-optimized diagonal loading and the Rao-Blackwell Leidot-Wolf estimator in terms of covariance-matrix-specific metrics.
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