期刊
IEEE SIGNAL PROCESSING LETTERS
卷 22, 期 11, 页码 2127-2131出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2015.2462724
关键词
Covariance estimation; diagonal loading; empirical Bayes; minimum mean square error (MMSE); regularization; robust estimation; shrinkage
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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