Sparse Bayesian linear regression with latent masking variables.

Extracting a small number of relevant features for the task, i.e., feature selection, is often a crucial step in supervised learning problems. Sparse linear regression provides a fast and convenient option for feature selection, where regularization facilitates reducing the weight parameters of irrelevant features. However, the regularization also induces undesirable shrinkage in the weights of relevant features.