论文标题
L0LEARN：使用L0正则化的可扩展软件包用于稀疏学习
L0Learn: A Scalable Package for Sparse Learning using L0 Regularization
论文作者
论文摘要
我们提供L0LEARN：使用$ \ ell_0 $正则化的开源软件包，用于稀疏线性回归和分类。 L0learn基于坐标下降和局部组合优化实现可扩展的近似算法。该软件包是使用C ++构建的，并具有用户友好的R和Python接口。 L0learn可以解决数百万个功能的问题，通过最先进的稀疏学习包来实现竞争性运行时间和统计性能。 L0LEARN可在Cran和GitHub（https://cran.r-project.org/package=l0learn和https://github.com/hazimehh/l0learn）上使用。
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).
