Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python
This provides a tool for researchers and practitioners working with high-dimensional data, but it is incremental as it builds on existing optimization methods.
The authors introduced picasso, a library implementing pathwise coordinate optimization for various sparse learning problems, which scales efficiently to large datasets.
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.