计算机应用与软件2024，Vol.41Issue(7)：222-227,238,7.DOI:10.3969/j.issn.1000-386x.2024.07.033
基于图的自适应加权多视图聚类
ADAPTIVE WEIGHTED MULTI-VIEW CLUSTERING BASED ON GRAPH
摘要
Abstract
Aimed at the existing graph-based multi-view clustering algorithms without considering the weight of different views and their problem of noise in view data,a graph-based adaptive weighted multi-view clustering algorithm is proposed.Multiple relational graphs were constructed from the original data through adaptive neighborhood learning,and the view weight adjustment parameters were introduced to reduce the influence of noise.Each graph was integrated into a unified graph by adaptive learning,and the data points were automatically divided into clusters by rank constraint optimization,so as to obtain the clustering results.Experimental results on multi-view data sets show the effectiveness of the proposed algorithm.关键词
多视图聚类/数据融合/自适应加权/拉普拉斯矩阵Key words
Multi-view clustering/Data fusion/Adaptive weighted/Laplacian matrix分类
信息技术与安全科学引用本文复制引用
蓝健,王俊义,林基明..基于图的自适应加权多视图聚类[J].计算机应用与软件,2024,41(7):222-227,238,7.基金项目
国家自然科学基金项目(61966007). （61966007）
