计算机科学与探索2025，Vol.19Issue(4)：964-975,12.DOI:10.3778/j.issn.1673-9418.2406069
面向无监督特征提取的结构化稀疏图学习
Structured Sparsity Graph Learning for Unsupervised Feature Extraction
摘要
Abstract
Unsupervised feature extraction has garnered increasing attention for alleviating the"curse of dimensionality"problem posed by high-dimensional data.However,existing methods typically construct low-rank graphs or nearest neigh-bor graphs to find the projection direction of high-dimensional data,overlooking the global structural correlation and spar-sity of representation.To address these issues,a novel dimensionality reduction method called structured sparse graph learning-based unsupervised feature extraction(SSGL)is proposed.The SSGL method utilizes representation to construct nearest neighbor graphs between samples to preserve the local structure of the data and uses least squares regression to model the global structural correlation of the data.Consequently,the proposed SSGL can simultaneously preserve both the local and global structural correlations of the data.Moreover,SSGL employs sparse regularization to disconnect links between samples from different clusters in the affinity graph,thereby making the learned projection more discriminative.To validate the effectiveness of SSGL,extensive experiments are conducted on eight public image datasets.The results indicate that SSGL outperforms other advanced feature extraction methods in terms of clustering accuracy,significantly enhancing clustering results and classification performance.关键词
特征提取/稀疏图/亲和关系/局部结构Key words
feature extraction/sparse graph/affinity relationship/local structure分类
信息技术与安全科学引用本文复制引用
朱奕珂,丁建浩,尹学松,王毅刚..面向无监督特征提取的结构化稀疏图学习[J].计算机科学与探索,2025,19(4):964-975,12.基金项目
浙江省基础性公益技术应用研究(LGG22F020032) （LGG22F020032）
温州市基础性公益技术应用研究(G2023093) （G2023093）
浙江省重点研发计划重点专项(2021C03137).This work was supported by the Public-Welfare Technology Application Research of Zhejiang Province(LGG22F020032),the Basic Public-Welfare Research Project of Wenzhou(G2023093),and the Key Research and Development Project of Zhejiang Province(2021C03137). （2021C03137）
