# Unsupervised Feature Selection via Robust Autoencoder and Adaptive Graph Learning

arXiv: https://arxiv.org/abs/2512.18720 · HTML: https://mlsanity.com/paper/2512.18720/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 52 references that are in the map.
- First posted on arXiv 21 Dec 2025.

## Builds on (most cited first, 11 of 11)

- [Coordinate Descent Algorithms](https://mlsanity.com/paper/1502.04759/) (2015)
- [Robust Subspace Recovery Layer for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.00152/) (2019)
- [An Overview of Robust Subspace Recovery](https://mlsanity.com/paper/1803.01013/) (2018)
- [Fast, Robust and Non-convex Subspace Recovery](https://mlsanity.com/paper/1406.6145/) (2014)
- [A generalized power iteration method for solving quadratic problem on the Stiefel manifold](https://mlsanity.com/paper/1701.00381/) (2017)
- [Fractal Autoencoders for Feature Selection](https://mlsanity.com/paper/2010.09430/) (2020)
- [A Subspace-Constrained Tyler's Estimator and its Applications to Structure from Motion](https://mlsanity.com/paper/2404.11590/) (2024)
- [Theoretical Guarantees for the Subspace-Constrained Tyler's Estimator](https://mlsanity.com/paper/2403.18658/) (2024)
- [Hyperparameter Estimation for Sparse Bayesian Learning Models](https://mlsanity.com/paper/2401.02544/) (2024)
- [Robust Sufficient Dimension Reduction via α-Distance Covariance](https://mlsanity.com/paper/2402.00778/) (2024)
- [Fr'echet Sufficient Dimension Reduction for Metric Space-Valued Data via Distance Covariance](https://mlsanity.com/paper/2412.13122/) (2024)

## Abstract (arXiv)

Effective feature selection is essential for high-dimensional data analysis and machine learning. Unsupervised feature selection (UFS) aims to simultaneously cluster data and identify the most discriminative features. Most existing UFS methods linearly project features into a pseudo-label space for clustering, but they suffer from two critical limitations: (1) an oversimplified linear mapping that fails to capture complex feature relationships, and (2) an assumption of uniform cluster distributions, ignoring outliers prevalent in real-world data. To address these issues, we propose the Robust Autoencoder-based Unsupervised Feature Selection (RAEUFS) model, which leverages a deep autoencoder to learn nonlinear feature representations while inherently improving robustness to outliers. We further develop an efficient optimization algorithm for RAEUFS. Extensive experiments demonstrate that our method outperforms state-of-the-art UFS approaches in both clean and outlier-contaminated data settings.