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基于加权锚点的多视图聚类算法

刘溯源 王思为 唐厂 周思航 王思齐 刘新旺

刘溯源, 王思为, 唐厂, 周思航, 王思齐, 刘新旺. 基于加权锚点的多视图聚类算法. 自动化学报, 2024, 50(6): 1160−1170 doi: 10.16383/j.aas.c220531
引用本文: 刘溯源, 王思为, 唐厂, 周思航, 王思齐, 刘新旺. 基于加权锚点的多视图聚类算法. 自动化学报, 2024, 50(6): 1160−1170 doi: 10.16383/j.aas.c220531
Liu Su-Yuan, Wang Si-Wei, Tang Chang, Zhou Si-Hang, Wang Si-Qi, Liu Xin-Wang. Multi-view clustering with weighted anchors. Acta Automatica Sinica, 2024, 50(6): 1160−1170 doi: 10.16383/j.aas.c220531
Citation: Liu Su-Yuan, Wang Si-Wei, Tang Chang, Zhou Si-Hang, Wang Si-Qi, Liu Xin-Wang. Multi-view clustering with weighted anchors. Acta Automatica Sinica, 2024, 50(6): 1160−1170 doi: 10.16383/j.aas.c220531

基于加权锚点的多视图聚类算法

doi: 10.16383/j.aas.c220531
基金项目: 国家自然科学基金(61922088, 62006236, 62006237), 国防科技大学科研计划项目(ZK21-23, ZK20-10), 高性能计算国家重点实验室自主课题(202101-15)资助
详细信息
    作者简介:

    刘溯源:国防科技大学计算机学院硕士研究生. 主要研究方向为多视图学习. E-mail: suyuanliu@nudt.edu.cn

    王思为:国防科技大学计算机学院博士研究生. 主要研究方向为无监督多视图学习, 大规模聚类和深度无监督学习. E-mail: wangsiwei13@nudt.edu.cn

    唐厂:中国地质大学计算机学院教授. 主要研究方向为多视图学习. E-mail: tangchang@cug.edu.cn

    周思航:国防科技大学智能科学学院讲师. 主要研究方向为机器学习, 医学图像分析. E-mail: sihangjoe@gmail.com

    王思齐:国防科技大学计算机学院高性能计算国家重点实验室助理研究员. 主要研究方向为机器学习, 异常检测. 本文通信作者. E-mail: wangsiqi10c@gmail.com

    刘新旺:国防科技大学计算机学院教授. 主要研究方向为核学习, 无监督特征学习. E-mail: xinwangliu@nudt.edu.cn

Multi-view Clustering With Weighted Anchors

Funds: Supported by National Natural Science Foundation of China (61922088, 62006236, 62006237), Research Project of National University of Defense Technology (ZK21-23, ZK20-10), and Autonomous Project of State Key Laboratory of High Performance Computing (202101-15)
More Information
    Author Bio:

    LIU Su-Yuan Master student at the College of Computer, National University of Defense Technology. His main research interest is multi-view learning

    WANG Si-Wei Ph.D. candidate at the College of Computer, National University of Defense Technology. His research interest covers unsupervised multi-view learning, scalable clustering, and deep unsupervised learning

    TANG Chang Professor at the College of Computer, China University of Geosciences. His main research interest is multi-view learning

    ZHOU Si-Hang Lecturer at the College of Intelligent Science and Technology, National University of Defense Technology. His research interest covers machine learning and medical image analysis

    WANG Si-Qi Assistant professor at the State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology. His research interest covers machine learning and outlier/anomaly detection. Corresponding author of this paper

    LIU Xin-Wang Professor at the College of Computer, National University of Defense Technology. His research interest covers kernel learning and unsupervised feature learning

  • 摘要: 大规模多视图聚类旨在解决传统多视图聚类算法中计算速度慢、空间复杂度高, 以致无法扩展到大规模数据的问题. 其中, 基于锚点的多视图聚类方法通过使用整体数据集合的锚点集构建后者对于前者的重构矩阵, 利用重构矩阵进行聚类, 有效地降低了算法的时间和空间复杂度. 然而, 现有的方法忽视了锚点之间的差异, 均等地看待所有锚点, 导致聚类结果受到低质量锚点的限制. 为定位更具有判别性的锚点, 加强高质量锚点对聚类的影响, 提出一种基于加权锚点的大规模多视图聚类算法(Multi-view clustering with weighted anchors, MVC-WA). 通过引入自适应锚点加权机制, 所提方法在统一框架下确定锚点的权重, 进行锚图的构建. 同时, 为增加锚点的多样性, 根据锚点之间的相似度进一步调整锚点的权重. 在9个基准数据集上与现有最先进的大规模多视图聚类算法的对比实验结果验证了所提方法的高效性与有效性.
    1)  11 http://mkl.ucsd.edu/dataset/protein-fold-prediction/2 http://archive.ics.uci.edu/ml/datasets/Multiple+Features3 https://www.fruitfly.org/4 http://svcl.ucsd.edu/projects/crossmodal/5 https://www.ee.columbia.edu/ln/dvmm/CCV/6 http://staff.science.uva.nl/aloi/7 https://www.cs.tau.ac.il/wolf/ytfaces/
    2)  2http://archive.ics.uci.edu/ml/datasets/Multiple+Features
    3)  3https://www.fruitfly.org/
    4)  4http://svcl.ucsd.edu/projects/crossmodal/
    5)  5https://www.ee.columbia.edu/ln/dvmm/CCV/
    6)  6http://staff.science.uva.nl/aloi/
    7)  7https://www.cs.tau.ac.il/wolf/ytfaces/
  • 图  1  4个数据集上学习到的锚点权重

    Fig.  1  Learned anchor weights on four datasets

    图  2  目标函数值随迭代次数增长的变化曲线

    Fig.  2  The variation curves the objective function value with the increase of the number of iterations

    图  3  参数调整对聚类性能的影响

    Fig.  3  The influence of parameter tuning on clustering performance

    表  1  本文使用的主要符号

    Table  1  Summary of notations

    符号 定义
    $n$ 数据点数量
    $k$ 类别数
    $v$ 视图数
    $m$ 锚点数
    $d^{(p)}$ 第$p$个视图上数据的维度
    ${\boldsymbol{X}}^{(p)} \in \mathbf{R}^{d^{(p)} \times n}$ 第$p$个视图的数据矩阵
    ${\boldsymbol{A}}^{(p)} \in \mathbf{R}^{d^{(p)} \times m}$ 第$p$个视图的锚点矩阵
    ${\boldsymbol{Z}}^{(p)} \in \mathbf{R}^{m \times n}$ 第$p$个视图上的锚图
    ${\boldsymbol{W}}^{(p)} \in \mathbf{R}^{m \times m}$ 第$p$个视图上的权重矩阵
    ${\boldsymbol{M}}^{(p)} \in \mathbf{R}^{m \times m}$ 第$p$个视图上锚点的相关性矩阵
    下载: 导出CSV

    表  2  实验中使用的数据集

    Table  2  Description of datasets

    数据集 样本数 视图数 类别数
    ProteinFold 694 12 27
    Mfeat 2 000 6 10
    BDGP 2 500 3 5
    Wiki 2 866 2 10
    CCV 6 773 3 20
    ALOI 10 800 4 100
    YTF10 38 654 4 10
    YTF20 63 896 4 20
    YTF50 126 054 4 50
    下载: 导出CSV

    表  3  对比算法在所有数据集上的聚类性能 (%)

    Table  3  Clustering performance of compared methods on all datasets (%)

    数据集
    MSC-IAS PMSC MVSC FMR SFMC MLRSSC AMGL RMKM BMVC LMVSC SMVSC FPMVS 本文算法
    ACC
    ProteinFold 28.45±1.31 12.06±0.41 24.83±1.35 32.85±1.75 26.22±0 11.10±0 10.96±1.23 23.63±0 26.22±0 28.29±1.57 29.26±1.52 30.03±1.06 32.57±1.88
    Mfeat 85.95±6.81 32.48±2.11 45.40±3.03 59.63±3.21 85.85±0 20.00±0 83.08±7.58 67.10±0 58.45±0 81.50±5.30 67.64±3.86 46.34±3.11 88.97±6.42
    BDGP 52.10±4.59 26.44±0.19 35.36±2.45 24.93±0.28 20.08±0 36.12±0 32.33±1.82 41.44±0 29.48±0 50.16±0.29 37.22±2.03 32.62±0.71 60.04±1.89
    Wiki 23.91±0.58 49.93±3.46 20.99±0.50 41.97±1.26 35.45±0 15.77±0 12.21±0.16 17.34±0 15.11±0 56.05±2.65 52.47±3.53 51.18±2.54 56.55±2.03
    CCV 11.93±0.26 12.52±0 10.44±0 13.71±0.31 11.94±0 15.50±0 20.28±0.60 22.98±0.58 22.88±0.74 22.60±0.67
    ALOI 1.01±0 60.26±1.69 33.74±0 59.67±0 40.27±1.55 48.34±1.49 21.72±0.65 71.29±1.80
    YTF10 75.68±0 60.43±0 66.74±3.69 72.93±3.96 67.09±2.80 79.15±8.39
    YTF20 57.62±0 60.09±0 60.64±4.18 67.13±4.20 63.08±2.39 68.16±4.82
    YTF50 66.00±0 68.32±2.45 67.13±3.68 64.24±2.97 66.97±3.08
    NMI
    ProteinFold 36.91±0.89 6.71±0.58 34.45±1.58 40.69±1.13 31.02±0 0±0 20.02±2.19 34.83±0 29.53±0 37.43±1.14 39.94±1.40 37.75±0.99 43.34±1.19
    Mfeat 87.68±2.85 40.14±2.76 42.49±3.30 49.19±1.37 91.77±0 28.63±0 87.29±3.84 65.33±0 68.88±0 79.35±1.95 62.18±1.77 56.46±1.81 86.74±2.26
    BDGP 33.07±2.81 3.70±0.20 10.25±2.15 0.99±0.08 2.25±0 26.33±0 13.42±2.29 28.12±0 4.60±0 25.41±0.15 9.85±1.22 10.02±0.38 33.78±0.43
    Wiki 8.65±0.27 52.01±1.51 7.28±0.67 33.09±1.09 34.18±0 0.08±0 0.82±0.10 4.34±0 2.46±0 51.57±2.17 50.05±3.79 49.34±2.95 49.47±1.77
    CCV 7.04±0.32 5.44±0 0±0 12.52±0.40 7.76±0 11.70±0 16.28±0.46 17.55±0.32 16.96±0.68 17.02±0.49
    ALOI 0.02±0 75.29±0.90 63.55±0 75.67±0 54.38±1.88 72.51±0.50 55.39±0.29 83.15±0.53
    YTF10 80.22±0 58.91±0 73.75±2.25 78.57±4.61 76.11±5.78 83.15±4.01
    YTF20 73.84±0 71.67±0 75.57±1.88 78.36±3.96 74.30±5.99 78.63±1.90
    YTF50 81.90±0 82.43±0.78 82.56±1.42 82.08±1.07 83.19±0.90
    Purity
    ProteinFold 32.99±1.37 14.37±0.41 31.26±1.19 38.46±1.60 28.96±0 11.10±0 11.71±1.20 33.86±0 28.53±0 35.90±1.63 36.00±1.16 34.95±0.66 39.21±1.56
    Mfeat 87.20±6.10 33.27±2.29 47.92±3.08 60.99±2.47 88.25±0 20.00±0 83.94±6.13 75.95±0 74.98±0 82.08±4.59 68.80±2.87 49.44±2.92 89.97±5.26
    BDGP 53.52±3.70 28.59±0.23 35.67±3.06 25.17±0.21 21.12±0 36.12±0 33.46±2.10 51.00±0 29.48±0 50.17±0.23 37.80±1.17 34.82±1.33 60.13±1.24
    Wiki 26.68±0.76 51.85±2.91 24.03±0.94 46.06±1.31 37.68±0 15.77±0 12.46±0.19 24.08±0 17.62±0 60.45±2.69 57.63±4.19 55.97±3.30 59.54±1.68
    CCV 15.92±0.31 13.04±0 10.44±0 14.12±0.33 17.04±0 19.18±0 23.62±0.47 25.91±0.51 25.09±0.78 25.34±0.67
    ALOI 1.01±0 63.92±1.26 64.02±0 62.35±0 42.32±1.55 51.46±1.41 23.67±0.72 73.81±1.42
    YTF10 80.70±0 60.43±0 71.52±3.25 77.35±5.70 69.43±3.06 83.57±5.78
    YTF20 68.78±0 64.83±0 68.20±3.02 72.40±3.79 64.92±1.95 74.40±3.32
    YTF50 73.64±0 73.21±2.18 70.09±3.61 66.84±3.02 73.65±2.50
    F-score
    ProteinFold 14.07±0.62 9.44±0.01 14.28±0.85 18.57±1.38 11.68±0 9.64±0 7.84±0.79 12.92±0 16.41±0 15.58±1.17 16.76±0.96 17.09±0.94 19.61±1.62
    Mfeat 83.66±6.35 26.94±1.09 37.46±2.69 41.49±1.51 85.52±0 27.39±0 81.39±7.35 59.22±0 62.59±0 74.42±4.13 56.50±2.45 46.57±1.33 85.05±5.09
    BDGP 40.44±2.22 29.55±0.10 29.08±0.61 21.00±0.07 33.15±0 41.19±0 32.62±0.81 36.28±0 26.51±0 37.81±0.06 28.81±1.23 28.79±0.58 45.31±0.50
    Wiki 15.44±0.25 41.83±2.91 14.91±0.54 30.34±0.78 21.38±0 19.46±0 12.48±0.69 13.04±0 11.15±0 48.71±2.18 45.76±4.69 44.91±3.43 47.17±1.64
    CCV 7.50±0.07 10.81±0 10.84±0 10.93±0.41 8.66±0 9.79±0 11.43±0.31 12.93±0.21 13.16±0.31 12.51±0.30
    ALOI 1.96±0 13.58±2.28 28.82±0 48.29±0 29.91±1.49 31.22±0.85 10.21±0.13 61.96±1.48
    YTF10 73.27±0 53.15±0 62.24±3.70 68.34±5.88 66.10±5.06 75.78±8.28
    YTF20 53.89±0 48.06±0 55.39±4.25 61.68±3.83 57.81±4.00 63.66±4.34
    YTF50 57.09±0 62.49±2.45 57.97±5.08 56.89±3.18 60.54±3.26
    下载: 导出CSV

    表  4  对比算法在所有数据集上的运行时间 (s)

    Table  4  Running time of compared methods on all datasets (s)

    数据集 MSC-IAS PMSC MVSC FMR SFMC MLRSSC AMGL RMKM BMVC LMVSC SMVSC FPMVS 本文算法
    ProteinFold 2.44 1 512.10 408.89 16.43 6.86 2.12 1.66 1.21 12.64 2.55 2.82 3.97 6.91
    Mfeat 16.81 3 300.30 11 528.00 251.03 88.62 27.94 19.62 3.95 0.43 2.96 1.38 1.43 9.20
    BDGP 13.26 15 215.00 34 800.00 1 070.40 39.00 26.89 73.71 7.53 0.35 2.86 1.63 3.38 7.18
    Wiki 15.92 14 386.00 9 991.70 1 068.80 9.84 30.72 180.62 6.27 0.11 3.57 3.15 20.16 4.89
    CCV 10 287.00 39.51 486.68 1 250.00 25.00 0.88 20.46 13.79 10.54 47.37
    ALOI 3 358.90 10 594.00 202.32 8.41 68.53 66.24 61.46 581.28
    YTF10 675.42 108.22 196.70 253.21 998.23 495.83
    YTF20 1 780.50 80.53 513.52 720.15 1 680.34 1 516.70
    YTF50 65.71 3 535.72 2 254.48 9 175.31 4 868.40
    下载: 导出CSV

    表  5  消融实验结果 (%)

    Table  5  Results of ablation experiments (%)

    聚类指标 对比方法 数据集
    ProteinFold Mfeat BDGP Wiki CCV ALOI YTF10 YTF20
    ACC 最优单视图 31.48±1.22 77.62±5.85 49.98±2.95 52.01±3.70 20.03±0.32 55.79±1.40 72.08±5.27 63.52±3.80
    未加权 27.83±1.66 82.55±6.64 46.32±3.19 52.05±2.38 18.10±0.53 70.14±2.04 70.72±8.29 66.36±4.72
    无正则化项 30.57±1.57 86.54±7.40 47.37±2.16 47.49±2.35 21.75±0.74 66.26±1.82 68.95±8.83 62.18±4.49
    本文方法 32.57±1.88 88.97±6.42 60.04±1.89 56.55±2.03 22.60±0.67 71.29±1.80 79.15±8.39 68.16±4.82
    NMI 最优单视图 41.08±0.82 74.73±2.25 27.61±2.33 50.01±3.12 16.67±0.40 73.59±0.44 74.87±2.52 69.70±1.55
    未加权 36.98±1.18 84.10±2.64 24.28±3.34 49.25±1.88 13.90±0.36 83.17±0.51 76.34±4.74 75.09±1.74
    无正则化项 42.10±1.08 87.26±2.59 26.89±2.87 36.51±2.07 16.83±0.49 79.91±0.51 76.77±4.39 75.65±1.70
    本文方法 43.34±1.19 86.74±2.26 33.78±0.43 49.47±1.77 17.02±0.49 83.15±0.53 83.15±4.01 78.63±1.90
    Purity 最优单视图 36.97±0.97 79.67±4.38 51.69±2.83 57.39±3.90 23.59±0.32 58.86±1.22 76.85±3.66 68.07±2.33
    未加权 35.17±1.46 84.32±5.43 47.12±3.11 58.34±2.52 21.10±0.40 72.77±1.71 76.89±6.26 71.52±3.27
    无正则化项 38.73±1.27 88.55±5.55 47.45±2.04 50.37±1.98 24.76±0.63 69.02±1.47 76.11±6.23 70.25±3.64
    本文方法 39.21±1.56 89.97±5.26 60.13±1.24 59.54±1.68 25.34±0.67 73.81±1.42 83.57±5.78 74.40±3.32
    F-score 最优单视图 19.63±1.10 69.72±4.28 37.83±2.14 45.07±3.62 11.50±0.20 43.10±1.38 67.00±4.92 52.49±3.96
    未加权 15.68±1.38 78.80±5.59 35.78±2.64 44.79±2.01 10.64±0.23 60.72±1.60 66.43±8.78 58.07±4.43
    无正则化项 18.65±1.24 83.90±5.96 38.61±2.13 37.17±1.75 12.06±0.32 54.92±1.47 67.00±8.70 54.88±3.74
    本文方法 19.61±1.62 85.05±5.09 45.31±0.50 47.17±1.64 12.51±0.30 61.96±1.48 75.78±8.28 63.66±4.34
    下载: 导出CSV
  • [1] Vidal R. Subspace clustering. IEEE Signal Processing Magazine, 2011, 28(2): 52−68 doi: 10.1109/MSP.2010.939739
    [2] 王卫卫, 李小平, 冯象初, 王斯琪. 稀疏子空间聚类综述. 自动化学报, 2015, 41(8): 1373−1384 doi: 10.16383/j.aas.2015.c140891

    Wang Wei-Wei, Li Xiao-Ping, Feng Xiang-Chu, Wang Si-Qi. A survey on sparse subspace clustering. Acta Automatica Sinica, 2015, 41(8): 1373−1384 doi: 10.16383/j.aas.2015.c140891
    [3] 张祎, 孔祥维, 王振帆, 付海燕, 李明. 基于多视图矩阵分解的聚类分析. 自动化学报, 2018, 44(12): 2160−2169 doi: 10.16383/j.aas.2018.c160636

    Zhang Yi, Kong Xiang-Wei, Wang Zhen-Fan, Fu Hai-Yan, Li Ming. Matrix factorization for multi-view clustering. Acta Automatica Sinica, 2018, 44(12): 2160−2169 doi: 10.16383/j.aas.2018.c160636
    [4] Wang S W, Liu X W, Zhu E, Tang C, Liu J Y, Hu J T, et al. Multi-view clustering via late fusion alignment maximization. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macao, China: Morgan Kaufmann, 2019. 3778−3784
    [5] Zhou S H, Nie D, Adeli E, Yin J P, Lian J, Shen D G. High-resolution encoder-decoder networks for low-contrast medical image segmentation. IEEE Transactions on Image Processing, 2020, 29(1): 461−475
    [6] Yang Y, Wang H. Multi-view clustering: A survey. Big Data Mining and Analytics, 2018, 1(2): 83−107 doi: 10.26599/BDMA.2018.9020003
    [7] Du S D, Liu Z H, Chen Z L, Yang W Y, Wang S P. Differentiable bi-sparse multi-view co-clustering. IEEE Transactions on Signal Processing, 2021, 69(1): 4623−4636
    [8] Liu X W, Zhou S H, Liu L, Tang C, Wang S W, Liu J Y, et al. Localized simple multiple kernel k-means. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2021. 9293−9301
    [9] Zhan K, Zhang C Q, Guan J P, Wang J S. Graph learning for multiview clustering. IEEE Transactions on Cybernetics, 2017, 48(10): 2887−2895
    [10] Nie F P, Cai G H, Li J, Li X L. Auto-weighted multi-view learning for image clustering and semi-supervised classification. IEEE Transactions on Image Processing, 2017, 27(3): 1501−1511
    [11] Jia Y H, Liu H, Hou J H, Kwong S, Zhang Q F. Multi-view spectral clustering tailored tensor low-rank representation. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(12): 4784−4797 doi: 10.1109/TCSVT.2021.3055039
    [12] Luo S R, Zhang C Q, Zhang W, Cao X C. Consistent and specific multi-view subspace clustering. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. New Orleans, Louisiana, USA: AAAI, 2018. 3730−3737
    [13] Ma Z R, Kang Z, Luo G C, Tian L, Chen W Y. Towards clustering-friendly representations: Subspace clustering via graph filtering. In: Proceedings of the 28th ACM International Conference on Multimedia. Seattle, USA: ACM, 2020. 3081−3089
    [14] 赵博宇, 张长青, 陈蕾, 刘新旺, 李泽超, 胡清华. 生成式不完整多视图数据聚类. 自动化学报, 2021, 47(8): 1867−1875 doi: 10.16383/j.aas.c200121

    Zhao Bo-Yu, Zhang Chang-Qing, Chen Lei, Liu Xin-Wang, Li Ze-Chao, Hu Qing-Hua. Generative model for partial multi-view clustering. Acta Automatica Sinica, 2021, 47(8): 1867−1875 doi: 10.16383/j.aas.c200121
    [15] Pan E, Kang Z. Multi-view contrastive graph clustering. Advances in Neural Information Processing Systems, 2021, 34: 2148−2159
    [16] Sun M J, Zhang P, Wang S W, Zhou S H, Tu W X, Liu X W, et al. Scalable multi-view subspace clustering with unified anchors. In: Proceedings of the 29th ACM International Conference on Multimedia. Chengdu, China: ACM, 2021. 3528−3536
    [17] Wang S W, Liu X W, Liu L, Tu W X, Zhu X Z, Liu J Y, et al. Highly-efficient incomplete large-scale multi-view clustering with consensus bipartite graph. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans, Louisiana, USA: IEEE, 2022. 9776−9785
    [18] Li Y Q, Nie F P, Huang H, Huang J Z. Large-scale multi-view spectral clustering via bipartite graph. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. Austin, Texas, USA: AAAI, 2015. 2750−2756
    [19] Li X L, Zhang H, Wang R, Nie F P. Multiview clustering: A scalable and parameter-free bipartite graph fusion method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 44(1): 330−344
    [20] Peng Z H, Liu H, Jia Y H, Hou J H. Adaptive attribute and structure subspace clustering network. IEEE Transactions on Image Processing, 2022, 31: 3430−3439 doi: 10.1109/TIP.2022.3171421
    [21] Lu C Y, Yan S C, Lin Z C. Convex sparse spectral clustering: Single-view to multi-view. IEEE Transactions on Image Processing, 2016, 25(6): 2833−2843 doi: 10.1109/TIP.2016.2553459
    [22] Wang Y, Wu L, Lin X M, Gao J B. Multiview spectral clustering via structured low-rank matrix factorization. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(10): 4833−4843 doi: 10.1109/TNNLS.2017.2777489
    [23] Gao H C, Nie F P, Li X L, Huang H. Multi-view subspace clustering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 2015. 4238−4246
    [24] Cao X C, Zhang C Q, Fu H Z, Liu S, Zhang H. Diversity-induced multi-view subspace clustering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 2015. 586−594
    [25] Zhang C Q, Fu H Z, Liu S, Liu G C, Cao X C. Low-rank tensor constrained multiview subspace clustering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 2015. 1582−1590
    [26] Brbic M, Kopriva I. Multi-view low-rank sparse subspace clustering. Pattern Recognition, 2018, 73(1): 247−258
    [27] Wang S W, Liu X W, Zhu X Z, Zhang P, Zhang Y, Gao F, et al. Fast parameter-free multi-view subspace clustering with consensus anchor guidance. IEEE Transactions on Image Processing, 2021, 31(1): 556−568
    [28] Liu S Y, Wang S W, Zhang P, Xu K, Liu X W, Zhang C W, et al. Efficient one-pass multi-view subspace clustering with consensus anchors. Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(7): 7576−7584
    [29] Kang Z, Zhou W T, Zhao Z T, Shao J M, Han M, Xu Z L. Large-scale multi-view subspace clustering in linear time. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. New York, USA: AAAI, 2020. 4412−4419
    [30] Wang X B, Lei Z, Guo X J, Zhang C Q, Shi H L, Li S Z. Multi-view subspace clustering with intactness-aware similarity. Pattern Recognition, 2019, 88(1): 50−63
    [31] Kang Z, Zhao X J, Peng C, Zhu H Y, Zhou J T, Peng X, et al. Partition level multiview subspace clustering. Neural Networks, 2020, 122(1): 279−288
    [32] Li R H, Zhang C Q, Hu Q H, Zhu P F, Wang Z. Flexible multi-view representation learning for subspace clustering. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macao, China: Morgan Kaufmann, 2019. 2916−2922
    [33] Nie F P, Li J, Li X L. Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence. New York, USA: Morgan Kaufmann, 2016. 1881−1887
    [34] Cai X, Nie F P, Huang H. Multi-view k-means clustering on big data. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence. Beijing, China: Morgan Kaufmann, 2013. 2598−2604
    [35] Zhang Z, Liu L, Shen F M, Shen H T, Shao L. Binary multi-view clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 41(7): 1774−1782
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出版历程
  • 收稿日期:  2022-06-27
  • 录用日期:  2022-11-12
  • 网络出版日期:  2022-12-19
  • 刊出日期:  2024-06-27

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