Multi-View Dynamic Kernelized Evidential Clustering  

作　　者：Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding 

机构地区：[1]the Software Engineering Institute,East China Normal University,Shanghai 200062 [2]Shanghai Key Laboratory of Trustworthy Computing,East China Normal University,Shanghai 200062,China [3]IEEE [4]the School of Automation,Northwestern Polytechnical University,Xi’an 710072,China [5]the School of Artificial Intelligence and Computer Science,Nantong University,Nantong 226019 [6]the Faculty of Data Science,City University of Macao,Macao 999078,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第12期2435-2450,共16页自动化学报(英文版)

基　　金：supported in part by the Youth Foundation of Shanxi Province(5113240053);the Fundamental Research Funds for the Central Universities(G2023KY05102);the Natural Science Foundation of China(61976120);the Natural Science Foundation of Jiangsu Province(BK20231337);the Natural Science Key Foundation of Jiangsu Education Department(21KJA510004)。

摘　　要：It is challenging to cluster multi-view data in which the clusters have overlapping areas.Existing multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering errors.Our solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping areas.MvDKE offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective function.Experiments on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关 键 词：Evidential clustering imprecision characterizing kernel technique multi-view clustering 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]