In this paper, we introduce a novel unsupervised, graph-based filter feature selection technique which exploits the power of topologically constrained network representations. We model dependency structures among features using a family of chordal graphs (the Triangulated Maximally Filtered Graph), and we maximise the likelihood of features' relevance by studying their relative position inside the network. Such an approach presents three aspects that are particularly satisfactory compared to its alternatives: (i) it is highly tunable and easily adaptable to the nature of input data; (ii) it is fully explainable, maintaining, at the same time, a remarkable level of simplicity; (iii) it is computationally cheaper compared to its alternatives. We test our algorithm on 16 benchmark datasets from different applicative domains showing that it outperforms or matches the current state-of-the-art under heterogeneous evaluation conditions.

翻译：本文提出了一种新颖的无监督、基于图的过滤式特征选择技术，该技术利用了拓扑约束网络表示的强大能力。我们使用一类弦图（三角化最大过滤图）对特征间的依赖结构进行建模，并通过研究特征在网络中的相对位置来最大化其相关性的似然。与现有替代方法相比，该方法具有三个尤其令人满意的方面：（i）高度可调且易于适应输入数据的特性；（ii）完全可解释，同时保持显著的简洁性；（iii）计算成本低于同类方法。我们在来自不同应用领域的16个基准数据集上测试了该算法，结果表明，在异构评估条件下，它优于或至少持平于当前最先进的水平。