Topological Feature Selection
This provides an efficient and explainable solution for feature selection in various domains, though it appears incremental as it builds on existing graph-based techniques.
The paper tackles the problem of unsupervised feature selection by introducing a graph-based method that models feature dependencies using chordal graphs, achieving performance that matches or exceeds the state-of-the-art on 16 benchmark datasets.
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.