Multi-view Clustering With Weighted Anchors

被引：0
|
作者：
Liu S.-Y. [1 ]
Wang S.-W. [1 ]
Tang C. [2 ]
Zhou S.-H. [3 ]
Wang S.-Q. [1 ,4 ]
Liu X.-W. [1 ]
机构：
[1] College of Computer, National University of Defense Technology, Changsha
[2] College of Computer, China University of Geosciences, Wuhan
[3] College of Intelligent Science and Technology, National University of Defense Technology, Changsha
[4] State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2024年 / 50卷 / 06期
基金：
中国国家自然科学基金;
关键词：
anchor; large-scale clustering; Multi-view clustering; weight learning;
D O I：
10.16383/j.aas.c220531
中图分类号：
学科分类号：
摘要：
Large-scale multi-view clustering aims to solve the problem that traditional methods cannot scale to large-scale data due to slow computational speed and high complexity. Among them, the anchor-based multi-view clustering method constructs a reconstruction matrix for the entire dataset by utilizing a set of anchor points. Clustering with the reconstruction matrix effectively reduces the time and space complexity of the algorithm. However, existing methods ignore the differences among anchor points and treat them equally, resulting in clustering results limited by low-quality anchor points. In order to identify more discriminative anchor points and enhance the influence of high-quality anchors on clustering, a large-scale multi-view clustering algorithm based on weighted anchors (MVC-WA) was proposed. By introducing an adaptive anchor weighting mechanism, the proposed method determine the weights of anchors in a unified framework for the construction of anchor graphs. Meanwhile, in order to increase the diversity among anchors, the weights of anchors were further adjusted according to the similarity between them. Experimental results comparing with existing state-of-the-art large-scale multi-view clustering algorithms on nine benchmark datasets validate the efficiency and effectiveness of the proposed method. © 2024 Science Press. All rights reserved.
引用
收藏
页码：1160 / 1170
页数：10
相关论文
共 35 条
  • [1] Vidal R., Subspace clustering, IEEE Signal Processing Magazine, 28, 2, (2011)
  • [2] Wang Wei-Wei, Li Xiao-Ping, Feng Xiang-Chu, Wang Si-Qi, A survey on sparse subspace clustering, Acta Automatica Sinica, 41, 8, pp. 1373-1384, (2015)
  • [3] Zhang Yi, Kong Xiang-Wei, Wang Zhen-Fan, Fu Hai-Yan, Li Ming, Matrix factorization for multi-view clustering, Acta Automatica Sinica, 44, 12, pp. 2160-2169, (2018)
  • [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, Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 3778-3784, (2019)
  • [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, 29, 1, (2020)
  • [6] Yang Y, Wang H., Multi-view clustering: A survey, Big Data Mining and Analytics, 1, 2, pp. 83-107, (2018)
  • [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, 69, 1, pp. 4623-4636, (2021)
  • [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, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9293-9301, (2021)
  • [9] Zhan K, Zhang C Q, Guan J P, Wang J S., Graph learning for multiview clustering, IEEE Transactions on Cybernetics, 48, 10, pp. 2887-2895, (2017)
  • [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, 27, 3, pp. 1501-1511, (2017)
1234