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, there
is a growing need for unsupervised feature selection algorithms focused on
imbalanced data. Thus, we propose a Marginal Laplacian Score (MLS) a
modification of the well-known Laplacian Score (LS) to be better suited for
imbalance 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 data set's margin. As MLS is better suited
for handling imbalanced data, 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 data sets.

高维度不平衡数据中，无监督特征选择方法对于后续算法的成功至关重要。为此，我们提出了一种更适用于不平衡数据的边缘拉普拉斯得分（Marginal Laplacian Score，MLS）算法，并将其集成到现代特征选择方法中，形成 DUFS-MLS。该方法在合成数据和公共数据集上展示了鲁棒性和改进性能。