基于自适应融合全局和局部信息的锚点多视图聚类

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中图分类号： TP301 文献标志码： A

文章编号： 1671-6841（2025）04-0030-10

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 selfrepresentation 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.

Key words： multi-view clustering; self-weighted; anchor; subspace clustering; spectral clustering; large-scale

0 引言

聚类算法可以探索数据的自然结构和分布以及其他有用的信息。（剩余15653字）