基于自相关矩阵的自适应多视图融合聚类算法

区卓越; 邓秀勤; 陈磊

doi:10.12052/gdutxb.240061

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基于自相关矩阵的自适应多视图融合聚类算法

计算机科学与技术 | 更新时间：2026-05-27

- 基于自相关矩阵的自适应多视图融合聚类算法
- Adaptive Multi-view-fusion Clustering Algorithm Based on Self-correlative Matrix
- 广东工业大学学报 2025年42卷第4期页码：29-38, 58
- 作者机构：
  
  广东工业大学数学与统计学院,广东,广州,510520
- 作者简介：
  
  区卓越 (2000–) ，男，硕士研究生，主要研究方向为机器学习、数据挖掘，E-mail：2112214088@mail2.gdut.edu.cn
  邓秀勤 (1966–)，女，教授，硕士生导师，主要研究方向为数据挖掘、机器学习，E-mail：dxq706@gdut.edu.cn
- 基金信息：
  
  广东省自然科学基金资助面上项目 (2024A1515010196);广东省研究生教育创新计划项目 (2021SFKC030)
- DOI：10.12052/gdutxb.240061
  中图分类号： TP391
- 纸质出版：2025
- 稿件说明：
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区卓越, 邓秀勤, 陈磊. 基于自相关矩阵的自适应多视图融合聚类算法[J]. 广东工业大学学报, 2025, 42(4): 29–38.

Ou Zhuoyue, Deng Xiuqin, Chen Lei. Adaptive Multi-view-fusion Clustering Algorithm Based on Self-correlative Matrix[J]. 2025, 42(4): 29-38, 58.
区卓越, 邓秀勤, 陈磊. 基于自相关矩阵的自适应多视图融合聚类算法[J]. 广东工业大学学报, 2025, 42(4): 29–38. DOI： 10.12052/gdutxb.240061

Ou Zhuoyue, Deng Xiuqin, Chen Lei. Adaptive Multi-view-fusion Clustering Algorithm Based on Self-correlative Matrix[J]. 2025, 42(4): 29-38, 58. DOI： 10.12052/gdutxb.240061.

摘要

在多视图聚类问题中，视图间的互补性信息与差异性信息会给聚类效果带来影响；同时样本点包含的重要性信息不同，也会对聚类效果产生不同的干扰。现有方法有些没有充分利用视图间的互补性信息，或者没有利用各视图间的差异性信息和样本点中的重要性信息，导致聚类效果不佳。针对上述问题，本文提出基于自相关矩阵的自适应多视图融合聚类算法(Adaptive Multi-view-fusion Clustering based on Self-correlative Matrix

AMCSM)。首先，使用特征直连技术，以更好地利用视图间的互补性信息；其次，使用自动权重机制为各视图自适应地分配适当的权重，以充分利用视图间的差异性信息；最后，对各视图施加对角的加权矩阵，并联合自相关矩阵以充分利用样本点中的重要性信息。设计统一的多步迭代框架将上述优化方案整合一起，使视图互补性信息、视图差异性信息与样本点重要性信息在迭代过程中相互促进、相互学习。实验结果表明，在灵敏度、精准度、特异度、调整兰德系数和马修斯相关系数等评价指标上，本文所提算法均取得优良结果且更具鲁棒性。

Abstract

In multi-view clustering problems

the complementary information and difference information between views have impact on the clustering effect

while the importance conveyed by the sample points also affects the clustering effect. Some existing methods do not fully utilize the complementary information between views

some do not consider the difference information between views

and some do not utilize the importance of sample points

resulting in poor clustering performance. To address the above issues

an adaptive multi-view-fusion clustering algorithm based on self-correlative matrix (AMCSM) is proposed. Firstly

feature concatenating is used to better utilize complementary information between views; Secondly

the auto-weighted mechanism is introduced to adaptively assign appropriate weights to each view

to fully utilize the difference information between views; Finally

diagonal weighted matrices and self-correlative matrices are simultaneously utilized to mine important information conveyed by the sample points. A unified multi-step iterative framework is designed to integrate the above optimization solutions

so that complementary information

difference information

and important information of sample points can promote and learn from each other during the iteration process. The experimental results show that the proposed algorithm achieves excellent results in evaluation metrics such as sensitivity

precision

specificity

adjusted Rand Index

and Matthews correlation coefficient

which is more robust.

关键词

Keywords

references

LI G, SONG D, BAI W, et al . Consensus and complementary regularized non-negative matrix factorization for multi-view image clustering[J ] . Information Sciences, 2023, 623: 524-538.

VON LUXBURG U. A tutorial on spectral clustering[J]. Statistics and computing, 2007, 17(4): 395-416.

HUANG S, XU Z, KANG Z, et al . Regularized nonnegative matrix factorization with adaptive local structure learning[J ] . Neurocomputing, 2020, 382: 196-209.

KHAN G A, HU J, LI T, et al . Multi-view data clustering via non-negative matrix factorization with manifold regularization[J ] . International Journal of Machine Learning and Cybernetics, 2022, 13: 677-689.

CHEN M, GONG M, LI X. Feature weighted non-negative matrix factorization[J]. IEEE Transactions on Cybernetics, 2023, 53(2): 1093-1105.

YAN H, LIU S, PHILIP S Y. From joint feature selection and self-representation learning to robust multi-view subspace clustering[C]//2019 IEEE international conference on data mining (ICDM) . Beijing: IEEE, 2019: 1414-1419.

WU S, LU Z, TANG H, et al . Joint learning of self-representation and indicator for multi-view image clustering[C ] //Proceedings of the IEEE International Conference on Image Processing (ICIP) . Taipei: IEEE, 2019: 4095-4099.

WANG H, YANG Y, LIU B. GMC: graph-based multi-view clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 32(6): 1116-1129.

ZHAN K, ZHANG C, GUAN J, et al . Graph learning for multiview clustering[J ] . IEEE Transactions on Cybernetics, 2018, 48(10): 2887-2895.

KANG Z, SHI G, HUANG S, et al . Multi-graph fusion for multi-view spectral clustering[J ] . Knowledge-Based Systems, 2020, 189: 105102.

LIU M, WANG Y, VASILE P, et al . Multi-view subspace clustering network with block diagonal and diverse representation[J ] . Information Sciences, 2023, 626: 149-165.

ZHANG F, HE W Q, JI H B, et al . Multi-view dictionary-pair learning based on block-diagonal representation[J ] . Computer Science, 2021, 48(1): 233-240.

XIA R, PAN Y, DU L, et al . Robust multi-view spectral clustering via low-rank and sparse decomposition[C ] //Proceedings of the AAAI Conference on Artificial Intelligence. Québec: AAAI, 2014, 28.

ZHONG J, WANG N, LIN Q, et al . Weighted feature selection via discriminative sparse multi-view learning[J ] . Knowledge-Based Systems, 2019, 178: 132-148.

OU Z, DENG X, CHEN L, et al . Adaptive multi-view subspace clustering algorithm based on representative features and redundant instances[J ] . Neurocomputing, 2024, 620: 128839.

KANG Z, ZHOU W, ZHAO Z, et al . Large-scale multi-view subspace clustering in linear time[C ] //Proceedings of the AAAI Conference on Artificial Intelligence. New York: AAAI, 2020: 4412-4419.

WANG S, LIU X, ZHU X, et al . Fast parameter-free multi-view subspace clustering with consensus anchor guidance[J ] . IEEE Transactions on Image Processing, 2021, 31: 556-568.

CHEN M, WANG C, HUANG D, et al . Efficient orthogonal multi-view subspace clustering[C ] //Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Washington D. C.: SIGKDD, 2022: 127-135.

ZHANG P, LIU X, XIONG J, et al . Consensus one-step multi-view subspace clustering[J ] . IEEE Transactions on Knowledge and Data Engineering, 2022, 34(10): 4676-4689.

LIU S Y, WANG S W, TANG C, et al . Multi-view clustering with weighted anchors[J ] . Acta Automatica Sinica, 2024, 50(6): 1160-1170.

DENG X, ZHANG Y, GU F. Fusion and enhancement of consensus matrix for multi-view subspace clustering[J]. Mathematics, 2023, 11: 1509.

NIE F, LI J, LI X. Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. New York: IJCAI, 2016: 1881-1887.

NIE F, LI J, LI X. Self-weighted multiview clustering with multiple graphs[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. Melbourne: IJCAI, 2017: 2564-2570.

ZHANG W, ZHANG Z B. Joint graph embedding and feature weighting for unsupervised feature selection[J]. Journal of Guangdong University of Technology, 2021, 38(5): 16-23.

LIU G, LIN Z, YU Y. Robust subspace segmentation by low-rank representation[C]//Proceedings of the 27th International Conference on Machine Learning. Haifa: ICML, 2010: 663-670.

CHICCO D, WARRENS J, JURMAN G. The Matthews correlation coefficient (MCC) is more informative than Cohen’s Kappa and Brier score in binary classification assessment[J]. IEEE Access, 2021, 9: 78368-78381.

CHICCO D, TöTSCH N, JURMAN G. The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation[J]. BioData Mining, 2021, 14(1): 1-22.

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