广东工业大学学报2025，Vol.42Issue(2)：37-51,15.
块对角引导的多视角一致性学习
Multi-view Consistency Learning with Block Diagonal Guidance
摘要
Abstract
Graph-based multi-view clustering methods are widely explored.However,there are still two problems with existing methods:1)Although some methods divide the similarity matrix into a consistency matrix and an inconsistency matrix,it is difficult to deal with the consistency information that has been misclassified in the inconsistency matrix,resulting in insufficient extraction of the valid information;and 2)Although some methods obtain a unified similarity matrix with a block diagonal structure,they do not remove redundancy information from the unified similarity matrix.To address these two issues,this paper proposes a Multi-view Consistency Learning with Block Diagonal Guidance(MCLBDG)method.First,we obtain a similarity matrix for each view via low rank representation and adaptive neighborhood.Second,we divide the similarity matrix of each view into a consistency matrix and an inconsistency matrix.The inconsistency part of different views is sieved via Hadamard product.During iterations,the misclassified consistency part can be gradually extracted from the inconsistency information.In addition,block diagonal guidance is proposed to remove the redundancy information in the unified similarity matrix as much as possible,which reduces the interference of extra-cluster samples.Finally,spectral clustering is incorporated into the model to obtain clustering results directly.Comparative experimental results on the commonly used datasets demonstrate the superiority of the method over the existing methods.关键词
块对角引导/多视角聚类/多视角图学习/一致性/无监督学习Key words
block diagonal guidance/multi-view clustering/multi-view graph learning/consistency/unsupervised learning分类
信息技术与安全科学引用本文复制引用
滕少华,韦晓杰,滕璐瑶,张巍..块对角引导的多视角一致性学习[J].广东工业大学学报,2025,42(2):37-51,15.基金项目
国家自然科学基金资助项目(61972102) （61972102）
广州市科技计划项目(2023A04J1729) （2023A04J1729）
