计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (1): 104-114.doi: 10.11896/jsjkx.241100070

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于多视图多样性学习的联合谱嵌入聚类算法

李顺勇1,2, 郑孟蛟1, 李嘉茗1, 赵兴旺3,4   

  1. 1 山西大学数学与统计学院 太原 030006;
    2 复杂系统与数据科学教育部重点实验室(山西大学) 太原 030006;
    3 山西大学计算机与信息技术学院 太原 030006;
    4 计算智能与中文信息处理教育部重点实验室(山西大学) 太原 030006
  • 收稿日期:2024-11-12 修回日期:2025-02-27 发布日期:2026-01-08
  • 通讯作者: 赵兴旺(zhaoxw@sxu.edu.cn)
  • 作者简介:(lisy75@sxu.edu.cn)
  • 基金资助:
    国家自然科学基金(82274360);山西省基础研究计划(202303021221054,202403021211086);山西省回国留学人员科研资助项目(2024-002);山西省留学回国人员科技活动择优项目(20250001);山西省研究生教育创新计划(2025SJ032,2025JG006)

Joint Spectrum Embedding Clustering Algorithm Based on Multi-view Diversity Learning

LI Shunyong1,2, ZHENG Mengjiao1, LI Jiaming1, ZHAO Xingwang3,4   

  1. 1 School of Mathematics and Statistics, Shanxi University, Taiyuan 030006, China;
    2 Key Laboratory of Complex Systems and Data Science of Ministry of Education(Shanxi University), Taiyuan 030006, China;
    3 School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China;
    4 Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education(Shanxi University), Taiyuan 030006, China
  • Received:2024-11-12 Revised:2025-02-27 Online:2026-01-08
  • About author:LI Shunyong,born in 1975,Ph.D,professor,is a member of CCF(No.U1748M).His main research interests include statistical machine learning and data mining.
    ZHAO Xingwang,born in 1984,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.22414M).His main research interests include data mining and machine learning.
  • Supported by:
    National Natural Science Foundation of China(82274360),Fundamental Research Program of Shanxi Province,China(202303021221054,202403021211086) and Research Project Supported by Shanxi Scholarship Council of China(2024-002),Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province(20250001) and Postgraduate Education Innovation Program of Shanxi Province(2025SJ032,2025JG006).

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摘要： 现有的大多数多视图聚类算法仅依赖于视图间的低阶相似性信息,未能有效地捕捉数据中的高阶结构特性,且对多视图数据的多样性特征关注不足,导致聚类结果的准确性和鲁棒性受限。针对以上问题,提出了一种基于多视图多样性学习的联合谱嵌入聚类算法——JSEC。首先通过视图多样性学习,保留数据间的多样特征,从而有效去除了视图中的噪声;然后提出了一种挖掘视图高阶信息的方法,使得视图的多样性特征尽可能靠近混合相似图,从而实现不同视图信息的高效整合,实现视图间的多样性和补充性融合;最后在谱嵌入模块将视图的多样性特征矩阵融合为联合谱嵌入矩阵,通过谱聚类实现图聚类。另外,设计了一种交替迭代的方法,用于优化目标函数。在与目前最新的多视图聚类算法的对比中,JSEC算法在5个中小规模的真实数据集的3个指标上均展现出优越的性能,同时在2个大规模数据集上也有优异的表现,相比次优算法,ARI指标在不同规模数据集上分别有1.27%和2.57%的提升,从而在理论和实验上验证了所提算法的稳健性。

关键词: 多视图聚类, 多样性学习, 高阶信息, 谱嵌入, 权重学习

Abstract: Most of the existing multi-view clustering algorithms only rely on the low-order similarity information between views,fail to capture the high-order structural features in the data effectively,and pay insufficient attention to the diversity features of the multi-view data,resulting in the accuracy and robustness of the clustering results.To solve these problems,JSEC,a joint spectral embedding clustering algorithm based on multi-view diversity learning,is proposed.Through view diversity learning,multiple features between data are preserved,so as to effectively remove the noise in the view.Then,a method of mining higher-order information of views is proposed to make the diversity features of views as close as possible to the hybrid similarity graph,so as to realize efficient integration of information of different views,and realize the diversity and complementary integration of views.Finally,the diversity feature matrix of the view is fused into the joint spectral embedding matrix in the spectral embedding module,and the graph clustering is realized by spectral clustering.In addition,an alternate iteration method is designed to optimize the objective function.In comparison with the latest multi-view clustering algorithms,JSEC algorithm shows superior performance on 3 indicators of 5 medium and small scale real datasets,and also on 2 large scale datasets.Compared with the suboptimal algorithm,ARI index has an improvement of 1.27% and 2.57% ondatasets of different scales.The superiority of the algorithm is proved theoretically and experimentally.

Key words: Multi-view clustering, Diversity learning, High-order information, Spectral embedding, Weight learning

中图分类号: 

  • TP311.13
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