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Random Matrix Improved Covariance Estimation for a Large Class of Metrics
Malik TIOMOKO A · Romain Couillet · Florent BOUCHARD · Guillaume GINOLHAC

Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #163

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 and faster. Applications to linear and quadratic discriminant analyses also show significant gains, therefore suggesting practical interest to statistical machine learning.

Author Information

Malik TIOMOKO A (Université Paris Sud)
Romain Couillet (CentralSupélec)
Florent BOUCHARD (LISTIC, Université Savoie Mont-Blanc)
Guillaume GINOLHAC (Université Savoie Mont-Blanc)

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