计算机应用研究2026，Vol.43Issue(4)：1112-1119,8.DOI:10.19734/j.issn.1001-3695.2025.08.0297
基于统一测量和张量学习的多视图无监督特征选择
Multi-view unsupervised feature selection based on unified measurement and tensor learning
摘要
Abstract
Multi-view data are increasingly common in high-dimensional industrial applications.Feature selection is important as it preserves the original meaning and interpretability of features.This is especially useful when labels are scarce,making multi-view unsupervised feature selection(MvUFS)highly practical.Existing methods often fall short in exploring inter-view relationships and consistency.To overcome this shortage,this paper proposed a new method called SMUMT.It integrated self-representation learning to improve sample representation.It also used joint learning to build a reliable similarity graph for gui-ding feature selection.Additionally,it introduced tensor learning to model high-order correlations across views.It conducted clustering experiments on seven public datasets.Results show that SMUMT outperforms six state-of-the-art methods in most cases.It performed particularly well on image datasets.These findings confirm that this method is effective for feature selection and improves clustering performance.关键词
张量学习/自表示学习/无监督特征选择Key words
tensor learning/self-representation learning/unsupervised feature selection分类
信息技术与安全科学引用本文复制引用
戴嘉珉,谢锡炯..基于统一测量和张量学习的多视图无监督特征选择[J].计算机应用研究,2026,43(4):1112-1119,8.基金项目
宁波市自然科学基金资助项目(2023J115) （2023J115）
