# Unsupervised Feature Ranking via Attribute Networks

arXiv: https://arxiv.org/abs/2111.13273 · HTML: https://mlsanity.com/paper/2111.13273/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 25 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis](https://mlsanity.com/paper/1606.04316/) (2016)
- [Feature Selection: A Data Perspective](https://mlsanity.com/paper/1601.07996/) (2016)
- [Variational inference for sparse network reconstruction from count data](https://mlsanity.com/paper/1806.03120/) (2018)

## Built on by (most cited first, 2 of 2)

- [G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network](https://mlsanity.com/paper/2301.12530/) (2023)
- [Iterative Feature Exclusion Ranking for Deep Tabular Learning](https://mlsanity.com/paper/2412.16442/) (2024)

## Abstract (arXiv)

The need for learning from unlabeled data is increasing in contemporary machine learning. Methods for unsupervised feature ranking, which identify the most important features in such data are thus gaining attention, and so are their applications in studying high throughput biological experiments or user bases for recommender systems. We propose FRANe (Feature Ranking via Attribute Networks), an unsupervised algorithm capable of finding key features in given unlabeled data set. FRANe is based on ideas from network reconstruction and network analysis. FRANe performs better than state-of-the-art competitors, as we empirically demonstrate on a large collection of benchmarks. Moreover, we provide the time complexity analysis of FRANe further demonstrating its scalability. Finally, FRANe offers as the result the interpretable relational structures used to derive the feature importances.