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arXiv · 2411.15197

K-means Derived Unsupervised Feature Selection using Improved ADMM

Ziheng Sun·Chris Ding·Jicong Fan
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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.

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Ziheng Sun, Chris Ding, Jicong Fan. 2024-11-19. K-means Derived Unsupervised Feature Selection using Improved ADMM. https://arxiv.org/abs/2411.15197

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