Fast Penalized Regression and Cross Validation for Tall Data with the oem Package

Fast Penalized Regression and Cross Validation for Tall Data with the oem Package

lasso, MCP, optimization, expectation maximization, C++, OpenMP, parallel
computing, out-of-memory computing

A large body of research has focused on theory and computation for variable selection
techniques for high dimensional data. There has been substantially less work in the
big “tall” data paradigm, where the number of variables may be large, but the number
of observations is much larger. The orthogonalizing expectation maximization (OEM)
algorithm is one approach for computation of penalized models which excels in the big
tall data regime. The oem package is an efficient implementation of the OEM algorithm
which provides a multitude of computation routines with a focus on big tall data, such as a
function for out-of-memory computation, for large-scale parallel computation of penalized
regression models. Furthermore, in this paper we propose a specialized implementation
of the OEM algorithm for cross validation, dramatically reducing the computing time for
cross validation over a naive implementation.

Jared D. Huling

https://www.jstatsoft.org/article/view/v104i06

University of Minnesota, Twin Cities

October 2022

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