Joint subspace learning and subspace clustering based unsupervised feature selection

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neurocomputing Pub Date : 2025-03-12 DOI:10.1016/j.neucom.2025.129885
Zijian Xiao , Hongmei Chen , Yong Mi , Chuan Luo , Shi-Jinn Horng , Tianrui Li
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Abstract

Unsupervised feature selection (UFS) has become a focal point of extensive research due to its ability to reduce the dimensionality of unlabeled data. Currently, many UFS methods based on subspace learning embed multiple graph regularization terms to preserve the local similarity structure of samples or features and rarely consider exploring global structure simultaneously, such as the self-representation structure between features and the potential clustering structure of samples. We propose a novel UFS model based on subspace learning and subspace orthogonal basis clustering (JSLSC) to address this problem. First, through robust subspace learning, JSLSC explores the self-representation information between the selected features and the original feature space. Features’ local and global structures are learned through feature selection and self-representation structure learning. Secondly, orthogonal basis clustering is introduced to learn the potential clustering structure in the low-dimensional sample space, thus enabling subspace clustering. Thirdly, hard-constrained graph structure learning is introduced to adaptively maintain the local structural consistency between low-dimensional samples and original samples. Finally, an optimization algorithm and convergence proof are proposed, and the superiority of the JSLSC is demonstrated through comparative experiments on nine real datasets.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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