广西师范大学学报（自然科学版）2024，Vol.42Issue(4)：137-152,16.DOI:10.16088/j.issn.1001-6600.2023110202
基于超图正则NMF的自适应半监督多视图聚类
Adaptive Semi-supervised Multi-view Clustering Based on Hypergraph Regular NMF
摘要
Abstract
Although graph regularized non-negative matrix factorization(GNMF)has become the basic framework for a large number of multi-view clustering methods,it is undoubtedly a great challenge to fuse complex data relationships from different views with a simple graph and obtain a consistent discriminative representation at the same time.In order to better deal with the clustering task of multi-view data,a semi-supervised multi-view clustering method based on hypergraph regularized non-negative matrix factorization is proposed.Specifically,by constructing a hypergraph,this method learns the high-order relationships of data from multiple views.In order to make reasonable use of the label information available in the real world,the label constraint is introduced for semi-supervised learning.In addition,this method considers the learning of consistency information and complementarity information at the same time,adopts adaptive measures to distinguish the contributions of different views,and uses an alternating iterative algorithm to optimize the objective function.The comparative experimental results on 7 real datasets show that the proposed algorithm is superior to other classical algorithms and current advanced algorithms in ACC and NMI indicators on 6 datasets.关键词
超图/非负矩阵分解/多视图聚类/半监督学习Key words
hypergraph/non-negative matrix factorization/multi-view clustering/semi-supervised learning分类
信息技术与安全科学引用本文复制引用
李向利,梅建平,莫元健..基于超图正则NMF的自适应半监督多视图聚类[J].广西师范大学学报（自然科学版）,2024,42(4):137-152,16.基金项目
国家自然科学基金(11961010,61967004) （11961010,61967004）
桂林电子科技大学研究生创新项目(2023YCXS115) （2023YCXS115）
