# K-means Derived Unsupervised Feature Selection using Improved ADMM

arXiv: https://arxiv.org/abs/2411.15197 · HTML: https://mlsanity.com/paper/2411.15197/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Sparse PCA via l2,p-Norm Regularization for Unsupervised Feature Selection](https://mlsanity.com/paper/2012.14595/) (2020)
- [Auto-weighted Multi-view Feature Selection with Graph Optimization](https://mlsanity.com/paper/2104.04906/) (2021)

## Abstract (arXiv)

Feature selection is important for high-dimensional data analysis and is non-trivial in unsupervised learning problems such as dimensionality reduction and clustering. The goal of unsupervised feature selection is finding a subset of features such that the data points from different clusters are well separated. This paper presents a novel method called K-means Derived Unsupervised Feature Selection (K-means UFS). Unlike most existing spectral analysis based unsupervised feature selection methods, we select features using the objective of K-means. We develop an alternating direction method of multipliers (ADMM) to solve the NP-hard optimization problem of our K-means UFS model. Extensive experiments on real datasets show that our K-means UFS is more effective than the baselines in selecting features for clustering.