Random matrix improved covariance estimation for a large class of metrics This article is an updated version of: Tiomoko M, Couillet R, Bouchard F and Ginolhac G 2019 Random matrix improved covariance estimation for a large class of metrics Proc. Machine Learning Research vol 97 pp 6254-63

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 method for a wide family of metrics. This method is shown to largely outperform the sample covariance matrix estima...

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Bibliographic Details
Published inJournal of statistical mechanics Vol. 2020; no. 12
Main Authors Tiomoko, Malik, Bouchard, Florent, Ginolhac, Guillaume, Couillet, Romain
Format Journal Article
LanguageEnglish
Published IOP Publishing and SISSA 21.12.2020
Subjects
machine learning
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Summary: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 method for a wide family of metrics. This 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 and faster. Applications to linear and quadratic discriminant analyses also show significant gains, therefore suggesting a practical relevance for statistical machine learning.
Bibliography:JSTAT_012P_0920
ISSN:1742-5468
DOI:10.1088/1742-5468/abcaf2