南京大学学报（自然科学版）2024，Vol.60Issue(1)：53-64,12.DOI:10.13232/j.cnki.jnju.2024.01.006
基于多阶近邻约束的深度不完整多视图聚类方法
Deep incomplete multi-view clustering based on multi-order neighborhood constraint
摘要
Abstract
Multi-view clustering is an important unsupervised learning method.However,in real applications,it is difficult to obtain complete multi-view data,which leads to incomplete multi-view clustering problem.Most of the existing incomplete multi-view clustering methods only consider the attribute information of views,but ignore the influence of structure information on clustering,resulting in extracted features cannot fully represent the latent structure of the original data.To address these problems,in this paper,a deep method based on multi-order neighborhood constraints is proposed for incomplete multi-view clustering.Firstly,the deep autoencoder with self-attention is used to obtain the rich complex latent features with cross-view information interaction,and the weighted fusion approach is employed to learn the consistency common information of views.Then,in incomplete multi-view settings,the missing data are fixed up by the consistency common representation of multi-views data.Finally,the multi-order neighborhood constraint mechanism is proposed,which considers the deep structural information within incomplete views and constructs an approximate complete neighborhood graph using the complementarity of multi-views,guiding the encoder to learn more compact and discriminative high-level semantic features.Experimental results show that the proposed method is effective.关键词
不完整多视图聚类/自注意力/结构信息/多阶近邻Key words
incomplete multi-view clustering/self-attention/structure information/multi-order neighborhood分类
计算机与自动化引用本文复制引用
王梅,王伟东,刘勇,于源泽..基于多阶近邻约束的深度不完整多视图聚类方法[J].南京大学学报（自然科学版）,2024,60(1):53-64,12.基金项目
国家自然科学基金(51774090,62076234),黑龙江省博士后科研启动金资助项目(LBH-Q20080) （51774090,62076234）
