用K-means目标优化无监督特征选择，提升聚类效果
K-means Derived Unsupervised Feature Selection using Improved ADMM
- 基于K-means目标设计特征选择机制
- 在多个真实数据集上优于现有基线方法
- 适合需要高效特征筛选的聚类任务
特征选择对高维数据分析至关重要，在无监督学习如降维和聚类中尤为关键。无监督特征选择的目标是选出能有效分离不同簇数据点的特征子集。本文提出一种新方法——基于K-means的无监督特征选择（K-means UFS）。与多数基于谱分析的方法不同，本方法直接以K-means目标为导向进行特征选择。为求解该方法带来的NP难优化问题，我们采用改进的交替方向乘子法（ADMM）。在多个真实数据集上的大量实验表明，K-means UFS在聚类任务中的特征选择效果优于现有基线方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。