Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python

TL;DR

Picasso is a versatile, efficient sparse learning library in R and Python that supports various models and regularizers, enabling scalable analysis of high-dimensional data.

Contribution

It introduces a unified coordinate optimization framework with active set strategies and supports multiple regularizers, enhancing flexibility and scalability in sparse learning.

Findings

Efficiently scales to large high-dimensional problems

Supports multiple regularizers including L1, MCP, SCAD

Demonstrates superior performance in numerical experiments

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 $\ell_1$, 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.