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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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Submitted on : Tuesday, May 19, 2020 - 1:48:44 PM
Last modification on : Tuesday, October 19, 2021 - 11:26:19 AM


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  • HAL Id : hal-02152121, version 1
  • ARXIV : 1902.02554


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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