双共识多视角谱聚类

陈曙; 朱正东; 杨祖元; 李珍妮

doi:10.12052/gdutxb.230072

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双共识多视角谱聚类

计算机科学与技术 | 更新时间：2026-05-27

- 双共识多视角谱聚类
- Co-consensus Multi-view Spectral Clustering
- 广东工业大学学报 2024年41卷第4期页码：98-105
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
  
  陈曙 (1997–) ，男，硕士研究生，主要研究方向为机器学习，E-mail：2112204096@mail2.gdut.edu.cn
  杨祖元 (1982–)，男，教授，博士，主要研究方向为机器学习、非负矩阵分解和盲信号处理，E-mail：yangzuyuan@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(62273106)
- DOI：10.12052/gdutxb.230072
  中图分类号： TP751
- 纸质出版：2024
- 稿件说明：
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陈曙, 朱正东, 杨祖元, 李珍妮. 双共识多视角谱聚类[J]. 广东工业大学学报, 2024, 41(4): 98-105.

Chen Shu, Zhu Zheng-dong, Yang Zu-yuan, et al. Co-consensus Multi-view Spectral Clustering[J]. 2024, 41(4): 98-105.
陈曙, 朱正东, 杨祖元, 李珍妮. 双共识多视角谱聚类[J]. 广东工业大学学报, 2024, 41(4): 98-105. DOI： 10.12052/gdutxb.230072.

Chen Shu, Zhu Zheng-dong, Yang Zu-yuan, et al. Co-consensus Multi-view Spectral Clustering[J]. 2024, 41(4): 98-105. DOI： 10.12052/gdutxb.230072.

摘要

多视角学习因其能融合各视角信息而受到广泛关注。针对多视角数据融合问题，本文提出了一种双共识多视角谱聚类方法，在谱聚类模型中添加两种共识约束，利用不同视角谱嵌入矩阵的特征关系和相似关系，增强多视角之间的一致性。同时，该方法在优化过程中能够获得相应共识变量的闭式解，进一步提升了聚类性能。实验在3个真实世界数据集中测试了该方法的收敛性及对参数的敏感性和聚类效果。实验结果表明，与现有的方法相比，本文的方法在多个性能指标上都有更好的表现，在聚类精度上最高提升超过10%。使用双共识方法可以有效提高多视角谱聚类的性能。

Abstract

Multi-view learning has attracted wide attention because of the ability to integrate information from different views. For the issue of multi-view data fusion

a co-consensus multi-view spectral clustering method is proposed. The method adds two consensus constraints in the model of spectral clustering to utilize the feature relationship and the similarity relationship of different views’ spectral embedding matrices which enhances the consistency of multi views. Simultaneously

this method obtains closed solution of the consensus variables in the optimization process

which further improves the clustering performance. The experiment tests the convergence

parameter sensitivity and clustering performance of the proposed method in three real-world datasets. The experiment results show that this method has the best performance in multiple performance metrics compared with the existing methods

and the maximum improvement in clustering accuracy is more than 10%. The experiment proves the co-consensus method effectively improves the performance of multi-view spectral clustering algorithm.

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references

JAIN A K, MURTY M N, FLYNN P J. Data clustering: a review [J]. ACM Computing Surveys (CSUR) , 1999, 31(3): 264-323.

XU R, WUNSCH D. Survey of clustering algorithms [J]. IEEE Transactions on Neural Networks, 2005, 16(3): 645-678 .

FOWLKES C, BELONGIE S, CHUNG F, et al . Spectral grouping using the nystrom method [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(2): 214-225.

XU D, TIAN Y. A comprehensive survey of clustering algorithms [J]. Annals of Data Science, 2015, 2: 165-193.

FU L, LIN P, VASILAKOS A V, et al . An overview of recent multi-view clustering [J ] . Neurocomputing, 2020, 402: 148-161.

YANG Y, WANG H. Multi-view clustering: a survey [J]. Big Data Mining and Analytics, 2018, 1(2): 83-107.

CHAO G, SUN S, BI J. A survey on multiview clustering [J]. IEEE Transactions on Artificial Intelligence, 2021, 2(2): 146-168.

LEE C K, LIU T L. Guided co-training for multi-view spectral clustering[C]//2016 IEEE International Conference on Image Processing (ICIP) . [S.l.]: IEEE, 2016: 4042-4046.

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

HAO W, PANG S, ZHU J, et al . Self-weighting and hypergraph regularization for multi-view spectral clustering [J ] . IEEE Signal Processing Letters, 2020, 27: 1325-1329.

LIANG W, ZHOU S, XIONG J, et al . Multi-view spectral clustering with high-order optimal neighborhood laplacian matrix [J ] . IEEE Transactions on Knowledge and Data Engineering, 2020, 34(7): 3418-3430.

ZHOU S, LIU X, LIU J, et al. Multi-view spectral clustering with optimal neighborhood Laplacian matrix[J]. Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(4) : 6965-6972.

JING P, SU Y, LI Z, et al . Learning robust affinity graph representation for multi-view clustering [J ] . Information Sciences, 2021, 544: 155-167.

HAO W, PANG S, CHEN Z. Multi-view spectral clustering via common structure maximization of local and global representations [J]. Neural Networks, 2021, 143: 595-606.

JIA Y, LIU H, HOU J, et al . Multi-view spec tral clustering tailored tensor low-rank representation [J ] . IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(12): 4784-4797.

ZHAO Y, YUN Y, ZHANG X, et al . Multi-view spectral clustering with adaptive graph learning and tensor schatten p-norm [J ] . Neurocomputing, 2022, 468: 257-264.

SHI S, NIE F, WANG R, et al . Self-weighting multi-view spectral clustering based on nuclear norm [J ] . Pattern Recognition, 2022, 124: 108429.

KUMAR A, RAI P, DAUME H. Co-regularized multi-view spectral clustering[J]. Advances in Neural Information Processing Systems, 2011, 24. 1413-1421.

HUANG L, LU J, TAN Y P. Co-learned multi-view spectral clustering for face recognition based on image sets [J]. IEEE Signal Processing Letters, 2014, 21(7): 875-879.

LU C, YAN S, LIN Z. Convex sparse spectral clustering: single-view to multi-view [J]. IEEE Transactions on Image Processing, 2016, 25(6): 2833-2843.

CHEN C, QIAN H, CHEN W, et al . Auto-weighted multi-view constrained spectral clustering [J ] . Neurocomputing, 2019, 366: 1-11.

XU H, ZHANG X, XIA W, et al . Low-rank tensor constrained co-regularized multi-view spectral clustering [J ] . Neural Networks, 2020, 132: 245-252.

ZHUGE W, LUO T, TAO H, et al . Multi-view spectral clustering with incomplete graphs [J ] . IEEE Access, 2020, 8: 99820-99831.

HAJJAR E, DORNAIKA F, ABDALLAH F. One-step multi-view spectral clustering with cluster label correlation graph [J]. Information Sciences, 2022, 592: 97-111.

HAJJAR E, DORNAIKA F, ABDALLAH F. Multi-view spectral clustering via constrained nonnegative embedding [J]. Information Fusion, 2022, 78: 209-217.

YANG W, WANG Y, TANG C, et al . One step multi-view spectral clustering via joint adaptive graph learning and matrix factorization [J ] . Neurocomputing, 2023, 524: 95-105.

DING C, HE X, SIMON H D. On the equivalence of nonnegative matrix factorization and spectral clustering[C]//Proceedings of the 2005 SIAM International Conference on Data Mining. [S.l.] : Society for Industrial and Applied Mathematics, 2005: 606-610.

HU Z, NIE F, CHANG W, et al . Multi-view spectral clustering via sparse graph learning [J ] . Neurocomputing, 2020, 384: 1-10.

HU Z, NIE F, WANG R, et al . Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding [J ] . Information Fusion, 2020, 55: 251-259.

TANG C, LI Z, WANG J, et al . Unified one-step multi-view spectral clustering [J ] . IEEE Transactions on Knowledge and Data Engineering, 2022, 35(6): 6449-6460.

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