Bayesian feature selection for high-dimensional linear regression via the Ising approximation with applications to genomics

摘要

Motivation: Feature selection, identifying a subset of variables that are relevant for predicting a response, is an important and challenging component of many methods in statistics and machine learning. Feature selection is especially difficult and computationally intensive when the number of variables approaches or exceeds the number of samples, as is often the case for many genomic datasets.