Not all real-world data are labeled, and when labels are not available, it is
often costly to obtain them. Moreover, as many algorithms suffer from the curse
of dimensionality, reducing the features in the data to a smaller set is often
of great utility. Unsupervised feature selection aims to reduce the number of
features, often using feature importance scores to quantify the relevancy of
single features to the task at hand. These scores can be based only on the
distribution of variables and the quantification of their interactions. The
previous literature, mainly investigating anomaly detection and clusters, fails
to address the redundancy-elimination issue. We propose an evaluation of
correlations among features to compute feature importance scores representing
the contribution of single features in explaining the dataset's structure.
Based on Coalitional Game Theory, our feature importance scores include a
notion of redundancy awareness making them a tool to achieve redundancy-free
feature selection. We show that the deriving features' selection outperforms
competing methods in lowering the redundancy rate while maximizing the
information contained in the data. We also introduce an approximated version of
the algorithm to reduce the complexity of Shapley values' computations.

本文提出了一种基于博弈理论、计算特征重要性的方法，用于无监督特征选择并消除冗余，结果表明该方法在降低冗余率的同时最大化数据信息。同时，本文还介绍了一种计算 Shapley 值的算法的近似版本，使其能够降低复杂度。