吉林大学学报（理学版）2025，Vol.63Issue(2)：513-527,15.DOI:10.13413/j.cnki.jdxblxb.2023509
基于自适应加权共识自表示的多视图子空间聚类
Multi-view Subspace Clustering Based on Adaptive Weighted Consensus Self-representation
摘要
Abstract
Aiming at the problem of how to fully integrate the complementary and diverse information of multi-view data to improve the clustering performance,we proposed a multi-view subspace clustering based on adaptive weighted consensus self-representation.Firstly,we introduced sparse mutual exclusion to learn view-specific sparse self-representation matrix,and then used adaptive weighted learning of multi-view consensus self-representation matrix to fuse the self-representation learned from various views.Secondly,we integrated the learning of multi-view consensus matrix and clustering indicator matrix into a unified optimization model,so that self-representation learning and clustering could promote each other.Finally,we conducted experiments on six commonly used multi-view datasets,and compared them with nine related methods.The experimental results show that the proposed method has obvious information fusion effect and improves clustering effect.关键词
多视图子空间聚类/稀疏表示/自表示/自适应加权学习Key words
multi-view subspace clustering/sparse representation/self-representation/adaptive weighted learning分类
信息技术与安全科学引用本文复制引用
李永,张维强..基于自适应加权共识自表示的多视图子空间聚类[J].吉林大学学报（理学版）,2025,63(2):513-527,15.基金项目
国家自然科学基金(批准号:61972264)、广东省自然科学基金(批准号:2019A1515010894)和深圳市高校稳定支持计划项目(批准号:20200807165235002). （批准号:61972264）
