/high-dim-ridge

The optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization

Primary LanguageJupyter Notebook

The optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization

Dmitry Kobak, Jonathan Lomond, Benoit Sanchez
https://arxiv.org/abs/1805.10939
Journal of Machine Learning Research, in press

optimal lambdas

The entire code is contained within the single Python notebook.