计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (7): 69-74.doi: 10.11896/jsjkx.240500097

• 计算机软件 • 上一篇    下一篇

基于跨视图二部图图扩散的多视图聚类

王劲夫1, 王思为2, 梁伟轩1, 于胜举1, 祝恩1   

  1. 1 国防科技大学计算机学院 长沙 410073
    2 智能博弈与决策实验室 北京 100071
  • 收稿日期:2024-05-22 修回日期:2024-09-03 发布日期:2025-07-17
  • 通讯作者: 祝恩(enzhu@nudt.edu.cn)
  • 作者简介:(wjf@nudt.edu.cn)
  • 基金资助:
    科技部重大项目(2022ZD020910)

Multi-view Clustering Based on Bipartite Graph Cross-view Graph Diffusion

WANG Jinfu1, WANG Siwei2, LIANG Weixuan1, YU Shengju1, ZHU En1   

  1. 1 School of Computer, National University of Defense Technology, Changsha 410073, China
    2 Intelligent Game and Decision Lab, Beijing 100071, China
  • Received:2024-05-22 Revised:2024-09-03 Published:2025-07-17
  • About author:WANG Jinfu,born in 1986,master.His main research interests include multi-view clustering and so on.
    ZHU En,born in 1976,professor,Ph.D supervisor,is a senior member of CCF(No.16689D).His main research in-terests include clustering,anomaly detection,computer vision,medical image analysis,etc.
  • Supported by:
    Major Projects of the Ministry of Science and Technology(2022ZD020910).

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摘要： 多视图聚类是无监督学习领域的一个研究热点。最近,基于跨视图图扩散的方法有效利用了多个视图之间的互补信息,取得了较好的效果。但这类方法的时间和空间复杂度较高,限制了其在大规模数据集上的应用。针对此问题,提出基于二部图跨视图图扩散的多视图聚类方法,成功将立方的时间复杂度和平方的空间复杂度降低至线性,从而可以高效地处理大规模聚类任务。使用二部图代替全图进行跨视图图扩散,并对基于全图的跨视图图扩散公式进行修改以适应二部图输入。在6个基准数据集上的实验结果表明,所提出的方法在聚类精度和运行效率方面比大多现有多视图聚类方法更具优势。在小规模数据集上,所提方法中的准确度等指标普遍高于对比算法5%以上;在大规模数据集上,所提方法的优势更加明显,其ACC和NMI等指标高于对比算法15%~30%。

关键词: 多视图聚类, 跨视图图扩散, 二部图, 大规模数据集应用

Abstract: Multi-view clustering is an research hotspots in the field of unsupervised learning.Recently,the method based on cross-view graph diffusion uses the complementary information between multiple views to obtain a unified graph for clustering on the basis of learning an improved graph for each view,which has achieved good results,but the time and space complexity are high,which limits its application on large-scale datasets.This paper proposes a multi-view clustering method based on bipartite graph diffusion across views to address problems with high time and space complexity.It reduces the complexity to linear complexity,making it suitable for large-scale clustering tasks.The specific method involves using a bipartite graph instead of a complete graph for cross-view graph diffusion and modifying the cross-view graph diffusion formula based on the complete graph to accommodate the bipartite graph input.Experimental results on six benchmark datasets demonstrate that the proposed method outperforms most existing multi-view clustering methods in terms of clustering accuracy and computational efficiency.In small-scale datasets,accuracy and other metrics are generally more than 5% higher than those of comparison algorithms.In large-scale datasets,the advantage is even more pronounced,with indicators such as ACC and NMI are 15%~30% higher than the comparison algorithms.

Key words: Multi-view clustering, Cross-view graph diffusion, Bipartite graph, Large-scale dataset applications

中图分类号: 

  • TP312
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