Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python
Abstract
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 $\ell_1$, nonvoncex MCP and SCAD regularizers. The library is coded in \texttt{C++} and has user-friendly R and Python wrappers. Numerical experiments demonstrate that picasso can scale up to large problems efficiently.
Cite
Text
Ge et al. "Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python." Machine Learning Open Source Software, 2019.Markdown
[Ge et al. "Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python." Machine Learning Open Source Software, 2019.](https://mlanthology.org/mloss/2019/ge2019jmlr-picasso/)BibTeX
@article{ge2019jmlr-picasso,
title = {{Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python}},
author = {Ge, Jason and Li, Xingguo and Jiang, Haoming and Liu, Han and Zhang, Tong and Wang, Mengdi and Zhao, Tuo},
journal = {Machine Learning Open Source Software},
year = {2019},
pages = {1-5},
volume = {20},
url = {https://mlanthology.org/mloss/2019/ge2019jmlr-picasso/}
}