块对角引导的多视角一致性学习

滕少华; 韦晓杰; 滕璐瑶; 张巍

doi:10.12052/gdutxb.230181

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块对角引导的多视角一致性学习

计算机科学与技术 | 更新时间：2026-05-27

- 块对角引导的多视角一致性学习
- Multi-view Consistency Learning with Block Diagonal Guidance
- 广东工业大学学报 2025年42卷第2期页码：37-51
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 广州番禺职业技术学院信息工程学院,广东,广州,511483
- 作者简介：
  
  滕少华(1962–)，男，教授，博士，主要研究方向为数据挖掘、协同计算、模式识别，E-mail：shteng@gdut.edu.cn
  滕璐瑶(1990–)，女，讲师，博士，主要研究方向为协同计算、模式识别，E-mail：luna.teng@qq.com
- 基金信息：
  
  国家自然科学基金资助项目(61972102);广州市科技计划项目(2023A04J1729)
- DOI：10.12052/gdutxb.230181
  中图分类号： TP181
- 纸质出版：2025
- 稿件说明：
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滕少华, 韦晓杰, 滕璐瑶, 等. 块对角引导的多视角一致性学习[J]. 广东工业大学学报, 2025, 42(2): 37–51.

Teng Shaohua, Wei Xiaojie, Teng Luyao, et al. Multi-view Consistency Learning with Block Diagonal Guidance[J]. 2025, 42(2): 37-51.
滕少华, 韦晓杰, 滕璐瑶, 等. 块对角引导的多视角一致性学习[J]. 广东工业大学学报, 2025, 42(2): 37–51. DOI： 10.12052/gdutxb.230181

Teng Shaohua, Wei Xiaojie, Teng Luyao, et al. Multi-view Consistency Learning with Block Diagonal Guidance[J]. 2025, 42(2): 37-51. DOI： 10.12052/gdutxb.230181.

摘要

基于图的多视角聚类方法得到了广泛的研究。然而，现有方法仍然存在两个问题：(1) 有些方法虽然将相似矩阵划分为一致性矩阵和不一致性矩阵，但难以处理被错误划分到不一致性矩阵中的一致性信息，导致有效信息不能得到充分提取。(2) 有些方法虽然得到了具有块对角结构的统一相似矩阵，但没有去除统一相似矩阵中的冗余信息。为了解决这两个问题，本文提出了一种块对角引导的多视角一致性学习(Multi-view Consistency Learning with Block Diagonal Guidance

MCLBDG) 方法。首先，该方法通过低秩表示和自适应邻域的方式获得每个视角的相似矩阵；其次，将每个视角的相似矩阵划分为一致性矩阵和不一致性矩阵。其中，不同视角的不一致性部分通过哈达玛积来筛选。在迭代过程中，被错误划分的一致性部分可以从不一致性信息中逐步提取出来。此外，提出了块对角引导来尽可能去除统一相似矩阵中的冗余信息，减少了不同簇样本之间的干扰。最后，将谱聚类应用到模型当中，直接得到聚类结果。在几个常用数据集上的比较实验验证了该方法的优越性。

Abstract

Graph-based multi-view clustering methods are widely explored. However

there are still two problems with existing methods: 1) Although some methods divide the similarity matrix into a consistency matrix and an inconsistency matrix

it is difficult to deal with the consistency information that has been misclassified in the inconsistency matrix

resulting in insufficient extraction of the valid information; and 2) Although some methods obtain a unified similarity matrix with a block diagonal structure

they do not remove redundancy information from the unified similarity matrix. To address these two issues

this paper proposes a Multi-view Consistency Learning with Block Diagonal Guidance (MCLBDG) method. First

we obtain a similarity matrix for each view via low rank representation and adaptive neighborhood. Second

we divide the similarity matrix of each view into a consistency matrix and an inconsistency matrix. The inconsistency part of different views is sieved via Hadamard product. During iterations

the misclassified consistency part can be gradually extracted from the inconsistency information. In addition

block diagonal guidance is proposed to remove the redundancy information in the unified similarity matrix as much as possible

which reduces the interference of extra-cluster samples. Finally

spectral clustering is incorporated into the model to obtain clustering results directly. Comparative experimental results on the commonly used datasets demonstrate the superiority of the method over the existing methods.

关键词

Keywords

references

DAFIR Z, LAMARI Y, SLAOUI S C. A survey on parallel clustering algorithms for big data[J]. Artificial Intelligence Review, 2021, 54(4): 2411-2443.

BHATTACHARJEE P, MITRA P. A survey of density-based clustering algorithms[J]. Frontiers of Computer Science (print) , 2020, 15(1): 151308.

CHEN M S, LIN J Q, LI X L, et al . Representation learning in multi-view clustering: a literature review[J ] . Data Science and Engineering, 2022, 7(3): 225-241.

XIE Y, ZHANG W S, QU Y Y, et al . Hyper-laplacian regularized multilinear multiview self-representations for clustering and semisupervised learning[J ] . IEEE Transactions on Cybernetics, 2020, 50(2): 572-586.

ZHANG C Q, FU H Z, HU Q H, et al . Generalized latent multi-view subspace clustering[J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(1): 86-99.

FENG Y C, YUAN Y, LU X Q. Person reidentification via unsupervised cross-view metric learning[J]. IEEE Transactions on Cybernetics, 2021, 51(4): 1849-1859.

FEI L K, ZHANG B, TENG S H, et al . Joint multiview feature learning for hand-print recognition[J ] . IEEE Transactions on Instrumentation and Measurement, 2020, 69(12): 9743-9755.

KANG Z, SHI G X, HUANG S D, et al . Multi-graph fusion for multi-view spectral clustering[J ] . Knowledge-Based Systems, 2019, 189: 105102.

NIE F P, CAI G H, LI X L. Multi-view clustering and semi-supervised classification with adaptive neighbors[C]//Thirty-first AAAI Conference on Artificial Intelligence. San Francisco: AAAI Press, 2017: 2408-2414.

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

WANG H, YANG Y, LIU B. GMC: graph-based multi-view clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 32: 1116-1129.

SHI S J, NIE F P, WANG R, et al . Auto-weighted multi-view clustering via spectral embedding[J ] . Neurocomputing, 2020, 399: 369-379.

LIANG Y W, HUANG D, WANG C D, et al . Multi-view graph learning by joint modeling of consistency and inconsistency[J ] . IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(2): 2848-2862.

HAO W Y, PANG S M, YANG B, et al . Tensor-based multi-view clustering with consistency exploration and diversity regularization[J ] . Knowledge Based Systems, 2022, 252: 109342.

ZHANG G Y, CHEN X W, ZHOU Y R, et al . Consistency-and inconsistency-aware multi-view subspace clustering[C ] //Database Systems for Advanced Applications: 26th International Conference, DASFAA 2021, Taipei: Springer International Publishing, 2021: 291-306.

FANG X Z, HAN N, WONG W K, et al . Flexible affinity matrix learning for unsupervised and semi supervised classification[J ] . IEEE Transactions on Neural Networks and Learning Systems, 2018, 30(4): 1133-1149.

ZHANG C Q, HU Q H, FU H Z, et al . Latent multi-view subspace clustering[C ] //Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . USA: IEEE Computer Society, 2017: 4279-4287.

XIE D Y, ZHANG X D, GAO Q X, et al . Multiview clustering by joint latent representation and similarity learning[J ] . IEEE Transactions on Cybernetics, 2019, 50(11): 4848-4854.

ZHOU T, ZHANG C Q, GONG C, et al . Multiview latent space learning with feature redundancy minimization[J ] . IEEE Transactions on Cybernetics, 2020, 50(4): 1655-1668.

MEI Y Y, REN Z W, WU B, et al . Robust graph-based multi-view clustering in latent embedding space[J ] . International Journal of Machine Learning and Cybernetics, 2022, 13: 497-508.

ZHENG Q H, ZHU J H, LI Z Y, et al . Feature concatenation multi-view subspace clustering[J ] . Neurocomputing, 2020, 379: 89-102.

LIANG N Y, YANG Z Y, LI Z N, et al . Multi-view clustering by non-negative matrix factorization with co-orthogonal constraints[J ] . Knowledge-Based Systems, 2020, 194: 105582.

TANG C, LIU X W, ZHU X Z, et al . Cgd: multi-view clustering via cross-view graph diffusion[J ] . Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(4): 5924-5931.

ZHONG G, SHU T, HUANG G H, et al . Multi-view spectral clustering by simultaneous consensus graph learning and discretization[J ] . Knowledge-Based Systems, 2022, 235: 107632.

LIU Y T, TENG S H, ZHANG W. Multiview consistency sparse clustering for graph[J]. Application Research of Computers, 2021, 38(8): 2315-2320.

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

LIANG Y C, ZHANG W, TENG S H. Multi-view unified graph clustering guided by block diagonal[J]. Journal of Chinese Computer Systems, 2023, 44(8): 1728-1734.

WANG Y, WU L, LIN X M, et al . Multi-view spectral clu stering via structured low-rank matrix factorization[J ] . IEEE Transactions on Networks and Learning Systems, 2018, 29(10): 4833-4843.

LIANG Y W, HUANG D, WANG C D. Consistency meets inconsistency: a unified graph learning framework for multi-view clustering[C]//IEEE International Conference on Data Mining (ICDM) . Beijing: IEEE, 2019: 1204-1209.

ZHAN K, NIE F P, WANG J, et al . Multiview consensus graph clustering[J ] . IEEE Transactions on Image Processing, 2019, 28(3): 1261-1270.

NIE F P, TIAN L, LI X L. Multiview clustering via adaptively weighted Procrustes[C]//Proceedings of the Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining. London: KDD, 2018: 2022-2030.

WEN J, ZHANG Z, ZHANG Z, et al . Generalized incomplete multiview clustering with flexible locality structure diffusion[J ] . IEEE Transactions on Cybernetics, 2020, 51(1): 101-114.

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