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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.
Cited literature [28 references]
https://hal.archives-ouvertes.fr/hal-02152121
Contributor : Guillaume Ginolhac <>
Submitted on : Tuesday, May 19, 2020 - 1:48:44 PM
Last modification on : Thursday, April 1, 2021 - 3:35:13 AM
Contributor : Guillaume Ginolhac <>
Submitted on : Tuesday, May 19, 2020 - 1:48:44 PM
Last modification on : Thursday, April 1, 2021 - 3:35:13 AM
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couillet_FisherCovEst.pdf
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