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基于子空间的多视图聚类算法因其良好的聚类性能和数学可解释性而备受关注。其中，一些基于锚点策略的大规模多视图子空间聚类算法，能够有效降低算法的时空复杂度。然而，现有的算法往往从全局结构中学习子空间自表示矩阵，忽视了视图数据、锚点和子空间自表示矩阵之间的局部结构信息。受多视图自加权多图学习算法的启发，提出了基于自适应融合全局和局部信息的锚点多视图聚类(AMVC-AFGL)算法。所提算法旨在通过自适应分配视图权重，融合数据之间的全局信息和局部信息，为每个视图数据学习一个更有效的子空间锚图矩阵，进而拼接为较小的融合锚图矩阵然后进行谱聚类。在公开的10个真实基准数据集上开展了充分的实验，结果表明，与其他12个先进的多视图聚类算法相比，所提算法具有有效性和可扩展性。
Abstract：Subspace-based multi-view clustering algorithms have attracted much attention due to their good clustering performance and mathematical interpretability. Among them, some large-scale multi-view subspace clustering algorithms based on anchor strategy can effectively reduce the spatiotemporal complexity. However, existing algorithms often learned the subspace self-representation matrix from the global structure, ignoring the local structure information between the view data, anchors and the subspace self-representation matrices. Inspired by the multi-view self-weighted multi-graph learning algorithm, the anchor multi-view clustering based on adaptive fusion of global and local information(AMVC-AFGL) algorithm was proposed. The proposed algorithm aimed to learn a more effective subspace anchor graph matrix for each view data by adaptively allocating view weights and fusing the global information and local information between the data, and then concatenated them into a smaller fusion anchor graph matrix for spectral clustering. Extensive experiments were carried out on 10 public real benchmark datasets, and compared with other 12 advanced multi-view clustering algorithms, the results showed the effectiveness and scalability of the proposed algorithm.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2023205
中图分类号:TP311.13
引用信息:
[1]冉戆,王思为,祝恩.基于自适应融合全局和局部信息的锚点多视图聚类[J].郑州大学学报(理学版),2025,57(04):30-39.DOI:10.13705/j.issn.1671-6841.2023205.
基金信息:
国家自然科学基金项目(62276271,62325604)
2023-08-31
2023
2025-10-24
2025
2024-04-01
3
2024-04-26
2024-04-26
2024-04-26