基于多阶近邻约束的深度不完整多视图聚类方法OA北大核心CSTPCD

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.