Screening Tests for Lasso Problems

TL;DR

This paper surveys dictionary screening methods for the lasso problem, which identify and remove irrelevant features to improve computational efficiency without affecting solution optimality.

Contribution

It provides a comprehensive overview of screening tests for lasso, including geometric insights and numerical evaluations of their effectiveness.

Findings

Screening reduces dictionary size and computational resources.

Screening tests can speed up lasso problem solving.

Limitations of screening methods are discussed.

Abstract

This paper is a survey of dictionary screening for the lasso problem. The lasso problem seeks a sparse linear combination of the columns of a dictionary to best match a given target vector. This sparse representation has proven useful in a variety of subsequent processing and decision tasks. For a given target vector, dictionary screening quickly identifies a subset of dictionary columns that will receive zero weight in a solution of the corresponding lasso problem. These columns can be removed from the dictionary prior to solving the lasso problem without impacting the optimality of the solution obtained. This has two potential advantages: it reduces the size of the dictionary, allowing the lasso problem to be solved with less resources, and it may speed up obtaining a solution. Using a geometrically intuitive framework, we provide basic insights for understanding useful lasso screening tests and their limitations. We also provide illustrative numerical studies on several datasets.