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arXiv · 1902.02554

Random Matrix Improved Covariance Estimation for a Large Class of Metrics

Malik Tiomoko·Florent Bouchard·Guillaume Ginholac·Romain Couillet
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Abstract

Relying on recent advances in statistical estimation of covariance distances based on random matrix theory, this article proposes an improved covariance and precision matrix estimation for a wide family of metrics. The method is shown to largely outperform the sample covariance matrix estimate and to compete with state-of-the-art methods, while at the same time being computationally simpler. Applications to linear and quadratic discriminant analyses also demonstrate significant gains, therefore suggesting practical interest to statistical machine learning.

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Malik Tiomoko, Florent Bouchard, Guillaume Ginholac, Romain Couillet. 2019-02-07. Random Matrix Improved Covariance Estimation for a Large Class of Metrics. https://doi.org/10.1088/1742-5468%2Fabcaf2

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