  High-dimensional imbalanced data poses a machine learning challenge. In the
absence of sufficient or high-quality labels, unsupervised feature selection
methods are crucial for the success of subsequent algorithms. Therefore, we
introduce a Marginal Laplacian Score (MLS), a modification of the well known
Laplacian Score (LS) tailored to better address imbalanced data. We introduce
an assumption that the minority class or anomalous appear more frequently in
the margin of the features. Consequently, MLS aims to preserve the local
structure of the dataset's margin. We propose its integration into modern
feature selection methods that utilize the Laplacian score. We integrate the
MLS algorithm into the Differentiable Unsupervised Feature Selection (DUFS),
resulting in DUFS-MLS. The proposed methods demonstrate robust and improved
performance on synthetic and public datasets.
