计算机应用研究2024，Vol.41Issue(11)：3357-3363,7.DOI:10.19734/j.issn.1001-3695.2024.04.0082
多角度语义标签引导的自监督多视图聚类
Multi-view clustering with self-supervised learning guided by multi-angle semantic labels
摘要
Abstract
Multi-view clustering aims to explore the feature information of objects from multiple perspectives to obtain accurate clustering results.However,existing research often fails to handle the information conflicts that arise during view fusion and does not fully utilize the complementary information between multiple views.To address these issues,this paper proposed a self-supervised multi-view clustering model guided by multi-angle semantic labels.The model first mapped the latent represen-tations of each view to independent low-dimensional feature spaces,focusing on optimizing the consistency between views in one space to maintain the local structure of the feature space and the relative relationships between samples.At the same time,in another space,clustering information was directly extracted from the view level to capture richer and more diverse semantic features.Finally,pseudo-labels generated from multi-angle semantic features guided the clustering assignment at the object level,achieving collaborative optimization of the two representations.Extensive experimental results demonstrate that this approach can comprehensively explore both common and complementary information in multi-view data and exhibit good cluste-ring performance.Moreover,compared to other methods,this approach has advantages in scenarios with a larger number of views.关键词
多视图聚类/无监督学习/对比学习/深度聚类Key words
multi-view clustering/unsupervised learning/contrastive learning/deep clustering分类
信息技术与安全科学引用本文复制引用
柳源,安俊秀,杨林旺..多角度语义标签引导的自监督多视图聚类[J].计算机应用研究,2024,41(11):3357-3363,7.基金项目
国家社会科学基金资助项目(22BXW048) （22BXW048）
四川省重点实验室开放基金资助项目(2024-ScL-MC&I-001) （2024-ScL-MC&I-001）
成都市科技重点研发支撑计划资助项目(2022-YF05-00454-SN) （2022-YF05-00454-SN）
