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4. Riemannian Framework For Robust Covariance Matrix Estimation In Spiked Models

Riemannian Framework For Robust Covariance Matrix Estimation In Spiked Models

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IEEE ICASSP 2020 Virtual Conference May 2020

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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Riemannian Framework For Robust Covariance Matrix Estimation In Spiked Models

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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