K-means Derived Unsupervised Feature Selection using Improved ADMM

TL;DR

This paper introduces K-means UFS, a novel unsupervised feature selection method that uses K-means objective and an improved ADMM algorithm to effectively select features for clustering in high-dimensional data.

Contribution

It proposes a new feature selection approach based on K-means objective and develops an ADMM algorithm to solve its NP-hard optimization problem.

Findings

K-means UFS outperforms baseline methods in clustering tasks.

The method effectively selects features that improve cluster separation.

Extensive experiments validate the approach's superiority.

Abstract

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.