# Unsupervised Feature Selection based on Adaptive Similarity Learning and Subspace Clustering

arXiv: https://arxiv.org/abs/1912.05458 · HTML: https://mlsanity.com/paper/1912.05458/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Unsupervised feature selection via self-paced learning and low-redundant regularization](https://mlsanity.com/paper/2112.07227/) (2021)
- [Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection](https://mlsanity.com/paper/2605.22973/) (2026)
- [Low-rank Dictionary Learning for Unsupervised Feature Selection](https://mlsanity.com/paper/2106.11102/) (2021)
- [Kernel Alignment for Unsupervised Feature Selection via Matrix Factorization](https://mlsanity.com/paper/2403.14688/) (2024)
- [Scalable unsupervised feature selection via weight stability](https://mlsanity.com/paper/2506.06114/) (2025)
- [Towards Truly Unsupervised Evaluation of Feature Selection -- Extended Version](https://mlsanity.com/paper/2608.12057/) (2026)

## Abstract (arXiv)

Feature selection methods have an important role on the readability of data and the reduction of complexity of learning algorithms. In recent years, a variety of efforts are investigated on feature selection problems based on unsupervised viewpoint due to the laborious labeling task on large datasets. In this paper, we propose a novel approach on unsupervised feature selection initiated from the subspace clustering to preserve the similarities by representation learning of low dimensional subspaces among the samples. A self-expressive model is employed to implicitly learn the cluster similarities in an adaptive manner. The proposed method not only maintains the sample similarities through subspace clustering, but it also captures the discriminative information based on a regularized regression model. In line with the convergence analysis of the proposed method, the experimental results on benchmark datasets demonstrate the effectiveness of our approach as compared with the state of the art methods.