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 and faster. Applications to linear and quadratic discriminant analyses also show significant gains, therefore suggesting practical interest to statistical machine learning.
Cite
Text
Tiomoko et al. "Random Matrix Improved Covariance Estimation for a Large Class of Metrics." International Conference on Machine Learning, 2019.Markdown
[Tiomoko et al. "Random Matrix Improved Covariance Estimation for a Large Class of Metrics." International Conference on Machine Learning, 2019.](https://mlanthology.org/icml/2019/tiomoko2019icml-random/)BibTeX
@inproceedings{tiomoko2019icml-random,
title = {{Random Matrix Improved Covariance Estimation for a Large Class of Metrics}},
author = {Tiomoko, Malik and Couillet, Romain and Bouchard, Florent and Ginolhac, Guillaume},
booktitle = {International Conference on Machine Learning},
year = {2019},
pages = {6254-6263},
volume = {97},
url = {https://mlanthology.org/icml/2019/tiomoko2019icml-random/}
}