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，该技术利用拓扑约束网络表征的能力。我们使用一族弦图（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。与其他替代方法相比，该方法在三个方面具有显著优势：(i) 高度可调且易于适应输入数据的特性；(ii) 完全可解释，同时保持极简性；(iii) 计算成本低于现有替代方案。我们在来自不同应用领域的16个基准数据集上测试算法，结果表明，在异质性评估条件下，该方法能够超越或匹配当前最先进方法的性能。