Fast Bayesian Feature Selection for High Dimensional Linear Regression in Genomics via the Ising Approximation
This method addresses computationally intensive feature selection for genomic datasets where variables often exceed samples, though it appears incremental as it builds on existing Bayesian and Ising model frameworks.
The paper tackles the challenge of feature selection in high-dimensional linear regression, common in genomics, by introducing the Bayesian Ising Approximation (BIA) to rapidly compute posterior probabilities for feature relevance, demonstrating its applicability on a gene expression dataset with nearly 30,000 features.
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. Here, we introduce a new approach -- the Bayesian Ising Approximation (BIA) -- to rapidly calculate posterior probabilities for feature relevance in L2 penalized linear regression. In the regime where the regression problem is strongly regularized by the prior, we show that computing the marginal posterior probabilities for features is equivalent to computing the magnetizations of an Ising model. Using a mean field approximation, we show it is possible to rapidly compute the feature selection path described by the posterior probabilities as a function of the L2 penalty. We present simulations and analytical results illustrating the accuracy of the BIA on some simple regression problems. Finally, we demonstrate the applicability of the BIA to high dimensional regression by analyzing a gene expression dataset with nearly 30,000 features.