Riemannian Framework For Robust Covariance Matrix Estimation In Spiked Models

This paper aims at providing an original Riemannian geometry to derive robust covariance matrix estimators in spiked models (i.e. when the covariance matrix has a low-rank plus identity structure). The considered geometry is the one induced by the product
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