信号处理2025，Vol.41Issue(2)：241-252,12.DOI:10.12466/xhcl.2025.02.004
基于公平感知的缺失多视图聚类
Incomplete Multi-View Clustering Based on Fairness Perception
江梦平 1刘美玲 1王前前 1高全学 1张向东1
作者信息
- 1. 西安电子科技大学通信工程学院,陕西 西安 710071
- 折叠
摘要
Abstract
Incomplete multi-view clustering is a technique for processing multi-source data that aims to identify consis-tent and complementary information across the data and segment it into distinct clusters.This method effectively ad-dresses the challenges of unsupervised multi-source data analysis in complex environments,making it a topic of consid-erable discussion.However,existing algorithms for incomplete multi-view clustering have notable shortcomings.They often overlook differences in the data arising from sensitive attributes associated with specific groups.This oversight can lead to biases against these groups,resulting in fairness issues during clustering.Furthermore,missing samples that are repaired may lose their uniqueness.To tackle these challenges,this paper presents a fairness-perception-based incom-plete multi-view clustering method.This approach aims to reduce the unfair treatment of underrepresented groups in un-supervised clustering tasks while addressing the issues of multi-view data consistency and missing data recovery.Ini-tially,an automated codec is trained for each view,allowing the coherent fusion of embedded features through informa-tion theory.Simultaneously,a generative network is trained to recover the missing view data.When utilizing the embed-ded features for clustering,we constrain the distribution of sensitive groups within each cluster.This ensures that the dis-tribution of these groups closely mirrors that of the entire dataset,promoting fairness in the algorithm.We conducted ex-periments comparing our method with five state-of-the-art incomplete multi-view clustering techniques across three widely used multi-view datasets.For instance,when the missing rate was 0.5 on the Bank dataset,our method achieved a 0.82% increase in Normalized Mutual Information(NMI)and a 3.03% increase in Balance compared to the second-best method.Additionally,on the Credit Card dataset,with a missing rate of 0,our method showed a 3.53% increase in NMI and a 5.62% increase in Balance compared to the second method.Visualization experiments on the Credit Card da-taset further confirmed the performance and fairness of our clustering algorithm.Ablation studies demonstrated the effec-tiveness of our proposed multi-view consistency fusion and missing view recovery mechanisms.Our method not only ad-dresses fairness concerns in unsupervised clustering within the context of incomplete multi-view data but also enhances the clustering performance of the algorithm.关键词
无监督学习/公平性机器学习/缺失多视图聚类/多视图一致性学习/缺失视图恢复/信息论Key words
unsupervised learning/fair machine learning/incomplete multi-view clustering/multi-view consistency learning/missing view recovery/information theory分类
信息技术与安全科学引用本文复制引用
江梦平,刘美玲,王前前,高全学,张向东..基于公平感知的缺失多视图聚类[J].信号处理,2025,41(2):241-252,12.
