# Infinite Feature Selection: A Graph-based Feature Filtering Approach

arXiv: https://arxiv.org/abs/2006.08184 · HTML: https://mlsanity.com/paper/2006.08184/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 16 of 16)

- [A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set](https://mlsanity.com/paper/2201.03349/) (2022)
- [An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation](https://mlsanity.com/paper/2201.00436/) (2021)
- [Fast Sparse PCA via Positive Semidefinite Projection for Unsupervised Feature Selection](https://mlsanity.com/paper/2309.06202/) (2023)
- [Open Continual Feature Selection via Granular-Ball Knowledge Transfer](https://mlsanity.com/paper/2403.10253/) (2024)
- [Topological Feature Selection](https://mlsanity.com/paper/2302.09543/) (2023)
- [Hard-Attention Gates with Gradient Routing for Endoscopic Image Computing](https://mlsanity.com/paper/2407.04400/) (2024)
- [Graph-Based Automatic Feature Selection for Multi-Class Classification via Mean Simplified Silhouette](https://mlsanity.com/paper/2309.02272/) (2023)
- [ELBD: Efficient score algorithm for feature selection on latent variables of VAE](https://mlsanity.com/paper/2111.08493/) (2021)
- [Graph-based Extreme Feature Selection for Multi-class Classification Tasks](https://mlsanity.com/paper/2303.01792/) (2023)
- [Top-k Regularization for Supervised Feature Selection](https://mlsanity.com/paper/2106.02197/) (2021)
- [Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2308.15484/) (2023)
- [EasyFS: an Efficient Model-free Feature Selection Framework via Elastic Transformation of Features](https://mlsanity.com/paper/2402.05954/) (2024)
- [Scalable unsupervised feature selection via weight stability](https://mlsanity.com/paper/2506.06114/) (2025)
- [Unsupervised Dynamic Feature Selection for Robust Latent Spaces in Vision Tasks](https://mlsanity.com/paper/2510.01758/) (2025)
- [Self-Attention And Beyond the Infinite: Towards Linear Transformers with Infinite Self-Attention](https://mlsanity.com/paper/2603.00175/) (2026)
- [When to Truncate a Feature Ranking: A Residual-Overlap Stopping Rule for Subset Selection](https://mlsanity.com/paper/2606.31686/) (2026)

## Abstract (arXiv)

We propose a filtering feature selection framework that considers subsets of features as paths in a graph, where a node is a feature and an edge indicates pairwise (customizable) relations among features, dealing with relevance and redundancy principles. By two different interpretations (exploiting properties of power series of matrices and relying on Markov chains fundamentals) we can evaluate the values of paths (i.e., feature subsets) of arbitrary lengths, eventually go to infinite, from which we dub our framework Infinite Feature Selection (Inf-FS). Going to infinite allows to constrain the computational complexity of the selection process, and to rank the features in an elegant way, that is, considering the value of any path (subset) containing a particular feature. We also propose a simple unsupervised strategy to cut the ranking, so providing the subset of features to keep. In the experiments, we analyze diverse settings with heterogeneous features, for a total of 11 benchmarks, comparing against 18 widely-known comparative approaches. The results show that Inf-FS behaves better in almost any situation, that is, when the number of features to keep are fixed a priori, or when the decision of the subset cardinality is part of the process.