  Contrastive analysis (CA) refers to the exploration of variations uniquely
enriched in a target dataset as compared to a corresponding background dataset
generated from sources of variation that are irrelevant to a given task. For
example, a biomedical data analyst may wish to find a small set of genes to use
as a proxy for variations in genomic data only present among patients with a
given disease (target) as opposed to healthy control subjects (background).
However, as of yet the problem of feature selection in the CA setting has
received little attention from the machine learning community. In this work we
present contrastive feature selection (CFS), a method for performing feature
selection in the CA setting. We motivate our approach with a novel
information-theoretic analysis of representation learning in the CA setting,
and we empirically validate CFS on a semi-synthetic dataset and four real-world
biomedical datasets. We find that our method consistently outperforms
previously proposed state-of-the-art supervised and fully unsupervised feature
selection methods not designed for the CA setting. An open-source
implementation of our method is available at https://github.com/suinleelab/CFS.
