  Feature selection is a technique to screen out less important features. Many
existing supervised feature selection algorithms use redundancy and relevancy
as the main criteria to select features. However, feature interaction,
potentially a key characteristic in real-world problems, has not received much
attention. As an attempt to take feature interaction into account, we propose
L1-LSMI, an L1-regularization based algorithm that maximizes a squared-loss
variant of mutual information between selected features and outputs. Numerical
results show that L1-LSMI performs well in handling redundancy, detecting
non-linear dependency, and considering feature interaction.
