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Random Matrix Improved Covariance Estimation for a Large Class of Metrics

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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Dates and versions

hal-02152121 , version 1 (19-05-2020)

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Malik Tiomoko, Florent Bouchard, Guillaume Ginolhac, Romain Couillet. Random Matrix Improved Covariance Estimation for a Large Class of Metrics. ICML 2019 - 36th International Conference on Machine Learning, Jun 2019, Long Beach, United States. ⟨hal-02152121⟩
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