Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (1): 104-114.doi: 10.11896/jsjkx.241100070

• Database & Big Data & Data Science • Previous Articles     Next Articles

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 Published: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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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

CLC Number: 

  • TP311.13
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