Description
The output of EmpiricalCovariance is regularized by a shrinkage value impacted by the overall mean of the data. The goal would be to implement this estimator with post-processing changes to the fitted empirical covariance. This project is very similar to the ShrunkCovariance project and would combine into a medium project. When implemented in python re-using our EmpiricalCovariance estimator, this would be an easy project with a small time commitment. Implementing the super-computing distributed version using python would only work for distributed-aware frameworks. Extended goals would make this a hard difficulty, medium commitment project. This would require implementing the regularization in C++ in oneDAL both for CPU and GPU. Then this must be made available in Scikit-learn-intelex for making a new estimator. This would hopefully follow the design strategy used for our Ridge Regression estimator.
https://scikit-learn.org/stable/modules/generated/sklearn.covariance.OAS.html