2023
JMLR
JMLR 2023
L0Learn: A Scalable Package for Sparse Learning using L0 Regularization
Abstract
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. [abs] [ pdf ][ bib ] [ code ] © JMLR 2023. (edit, beta)
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Regression
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Core Methods > Feature Selection
Machine Learning > Core Methods > Optimization
Machine Learning > Learning Types > Sparse Learning