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Random Matrix Improved Covariance Estimation for a Large Class of Metrics

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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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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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