L0Learn: A Scalable Package for Sparse Learning using L0 Regularization
This provides a scalable tool for researchers and practitioners in machine learning dealing with high-dimensional data, though it is incremental as it builds on existing sparse learning methods.
The authors tackled the problem of sparse linear regression and classification by developing L0Learn, an open-source package using L0 regularization, which achieves competitive run times and statistical performance for problems with millions of features.
We present L0Learn: an open-source package for sparse linear regression and classification using $\ell_0$ regularization. L0Learn implements scalable, approximate algorithms, based on coordinate descent and local combinatorial optimization. The package is built using C++ and has user-friendly R and Python interfaces. L0Learn can address problems with millions of features, achieving competitive run times and statistical performance with state-of-the-art sparse learning packages. L0Learn is available on both CRAN and GitHub (https://cran.r-project.org/package=L0Learn and https://github.com/hazimehh/L0Learn).