计算机应用与软件2024，Vol.41Issue(11)：309-318,10.DOI:10.3969/j.issn.1000-386x.2024.11.043
基于多样化流形学习的非线性矩阵分解数据聚类
NONLINEAR MATRIX FACTORIZATION DATA CLUSTERING BASED ON MANIFOLD LEARNING
摘要
Abstract
In order to capture the local geometric structure of multi-faceted data and improve the clustering performance,a nonlinear matrix factorization data clustering method based on manifold learning is proposed.A P-nearest neighbor graph was constructed for each relationship to capture two different types of closely related objects,so as to accurately learn the internal relations and multiple manifolds generated by the internal relations of data.And we stably kept the learned manifold when mapping to a new low dimensional data space with nonlinear matrix factorization.The clustering results of multiple data sets show that the method can fully mine the partial representation of various related types,and has certain advantages in accuracy and efficiency.关键词
多面数据/聚类/流形学习/P近邻图Key words
Multi-faceted data/Clustering/Manifold learning/P nearest neighbor graph分类
信息技术与安全科学引用本文复制引用
郑淦专,李原浩..基于多样化流形学习的非线性矩阵分解数据聚类[J].计算机应用与软件,2024,41(11):309-318,10.基金项目
湖南省自然科学基金重大项目(2015JJ2004). （2015JJ2004）
