论文标题
毕加索：用于R和Python中高维数据分析的稀疏学习库
Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python
论文作者
论文摘要
我们描述了一个名为Picasso的新库，该库实现了针对各种稀疏学习问题的路径坐标优化的统一框架（例如，稀疏线性回归，稀疏的逻辑回归，稀疏的Poisson回归和缩放的稀疏线性回归）与有效的活动设置选择策略相结合。此外，该库允许用户选择不同的稀疏性诱导正规化器，包括凸$ \ ell_1 $，nonconvex MCP和SCAD正则化器。该库在C ++中进行编码，并具有用户友好的R和Python包装器。数值实验表明，毕加索可以有效地扩展到大型问题。
We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e.g., sparse linear regression, sparse logistic regression, sparse Poisson regression and scaled sparse linear regression) combined with efficient active set selection strategies. Besides, the library allows users to choose different sparsity-inducing regularizers, including the convex $\ell_1$, nonconvex MCP and SCAD regularizers. The library is coded in C++ and has user-friendly R and Python wrappers. Numerical experiments demonstrate that picasso can scale up to large problems efficiently.
