计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (4): 138-146.doi: 10.11896/jsjkx.240100131

• 数据库&大数据&数据科学 • 上一篇    下一篇

一致块对角和限定的多视角子空间聚类算法

吴杰, 万源, 刘秋杰   

  1. 武汉理工大学理学院 武汉 430070
  • 收稿日期:2024-01-16 修回日期:2024-06-15 出版日期:2025-04-15 发布日期:2025-04-14
  • 通讯作者: 万源(wanyuan@whut.edu.cn)
  • 作者简介:(273226@whut.edu.cn)
  • 基金资助:
    中央高校基本科研业务费专项基金(2021III030JC)

Consistent Block Diagonal and Exclusive Multi-view Subspace Clustering

WU Jie, WAN Yuan, LIU Qiujie   

  1. School of Science,Wuhan University of Technology,Wuhan 430070,China
  • Received:2024-01-16 Revised:2024-06-15 Online:2025-04-15 Published:2025-04-14
  • About author:WU Jie,born in 1999,postgraduate.His main research interests include machine learning and pattern recognition.
    WAN Yuan,born in 1976,Ph.D,professor.Her main research interests include data mining,pattern recognition,manifold learning,and machine learning.
  • Supported by:
    Fundamental Research Funds for the Central Universities of Ministry of Education of China(2021III030JC).

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摘要： 子空间聚类方法为高维多视角数据的聚类问题提供了有效的解决方案。针对现有算法利用低秩或稀疏约束通过模型的特定性质不能使得表示矩阵直接具有块对角性的问题,提出了一致块对角和限定的多视角子空间聚类算法(CBDE-MSC)。CBDE-MSC将各个视角的子空间表示矩阵分解为一致自表示矩阵和特定自表示矩阵。对于一致自表示矩阵,使用块对角约束使其具有近似的块对角结构,探索数据的一致性;对于特定自表示矩阵,在其间施加限定性约束,探索数据的互补性。使用矩阵L2,1范数约束误差矩阵,使其满足行稀疏。此外,使用交替方向乘子法(ADMM)优化目标函数。采用归一化互信息(NMI)、正确率(ACC)、调整兰德指数(AR)和F分数(F-score)等评价指标,对CBDE-MSC进行了评估。实验结果表明,CBDE-MSC与现有的一些优良算法相比,4个指标的结果均有较大的提升,尤其是在YaleB数据集上,相比于经典方法CSMSC,其NMI,ACC,AR和F-score分别提升了0.088,0.127,0.145和0.122。实验结果验证了所提算法的有效性。

关键词: 子空间聚类, 多视角学习, 表示学习, 块对角表示

Abstract: Subspace clustering method provides an effective solution to the clustering problem of high-dimensional multi-view data.Focusing on the issue that the representation matrix cannot obey the block diagonal property directly by using low rank or sparse constraints in existing algorithms,a consistent block diagonal and exclusive multi-view subspace clustering(CBDE-MSC) is proposed.CBDE-MSCdecomposes the subspace representation matrix of each perspective into consistent and specific self-representation matrices.For the consistent self-representation matrix,block diagonal constraint is used to make it have an approximate block diagonal structure and explore the consistency of the data.The exclusive constraint is applied between specific self-representation matrices to explore the complementarity of data.The matrix L2,1 norm is used to constrain the error matrix so that it satisfies row sparsity.In addition,alternate direction multiplier method(ADMM) is used to optimize the objective function.CBDE-MSC is evaluated by normalized mutual information(NMI),accuracy(ACC),adjusted rand index(AR) and F-score.Expe-rimental results show that compared with some existing excellent algorithms,CBDE-MSC has a great improvement in the results of the four indicators,especially in the YaleB dataset,CBDE-MSC compared with the classical method CSMSC,NMI,ACC,AR and F-score increased by 0.088,0.127,0.145 and 0.122,which verifies the effectiveness of the proposed algorithm.

Key words: Subspace clustering, Multi-view learning, Representation learning, Block diagonal representation

中图分类号: 

  • TP391
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