Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python
نویسندگان
چکیده
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 sparse square root loss 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, has user-friendly R and Python wrappers, and can scale up to large problems efficiently with the memory optimized using sparse matrix output.
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