We describe a new library named picasso 1, 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 '1, nonvoncex 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.
|Original language||English (US)|
|Journal||Journal of Machine Learning Research|
|State||Published - Mar 1 2019|
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Statistics and Probability
- Artificial Intelligence