华南理工大学学报（自然科学版）2026，Vol.54Issue(2)：16-24,9.DOI:10.12141/j.issn.1000-565X.250145
基于样本互补锚点图的缺失多视图聚类算法
Incomplete Multi-View Clustering Algorithm Based on Sample Complementary Anchor Graph
摘要
Abstract
With the widespread application of multi-view data in real-world scenarios,clustering with incomplete views has emerged as a significant challenge in machine learning.Traditional anchor graph-based clustering algo-rithms rely on complete instances to build the anchor graphs.This dependency leads to insufficient anchors for cap-turing the underlying data structure under high missing rates,while failing to fully leverage the benefits of anchors when missing rate is low.To address the limitations of traditional methods,including restricted anchor selection,inflexible weight assignment,and high computational complexity,this paper proposed an incomplete multi-view clustering algorithm based on a Sample-Complementary Anchor Graphs(IMVC-SAC).First,the algorithm introduces a cross-view anchor complementation mechanism,which adaptively selects anchors from both shared samples and view-specific samples to enhance data structure representation,particularly under high missing rates.Second,it establishes a missing pattern-aware weighting model that dynamically adjusts the contribution of each view to the similarity matrix based on the missing pattern and degree of the samples.Finally,by leveraging the properties of doubly stochastic non-negative matrix factorization,the time complexity of spectral clustering is reduced from cubic to linear with respect to the sample size.Experimental results on five public datasets demonstrate that the proposed IMVC-SAC algorithm outperforms state-of-the-art methods in clustering performance.Notably,it maintains robust and effective clustering even under high missing rates,validating its superiority.关键词
缺失多视图聚类/锚点图/样本互补/相似矩阵融合/谱聚类Key words
incomplete multi-view clustering/anchor graph/sample complementarity/similarity matrix fusion/spectral clustering分类
信息技术与安全科学引用本文复制引用
刘小兰,徐宇鸿..基于样本互补锚点图的缺失多视图聚类算法[J].华南理工大学学报（自然科学版）,2026,54(2):16-24,9.基金项目
国家社会科学基金项目(21BTJ069) （21BTJ069）
广东省线上线下混合一流课程(粤教高函[2023]33号)Supported by the National Social Science Foundation of China(21BTJ069) （粤教高函[2023]33号）
