Incomplete Multi-View Clustering via Auto-Weighted Fusion in Partition Space
作者机构：School of Computing and Artificial IntelligenceSouthwest Jiaotong UniversityChengdu 611756China School of ComputerGuangxi University of Science and TechnologyLiuzhou 545006China
出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)
年 卷 期：2023年第28卷第3期
页 面：595-611页
核心收录：
学科分类：0401[教育学-教育学] 04[教育学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 040110[教育学-教育技术学（可授教育学、理学学位）]
基 金：Acknowledgment This work was supported by the National Natural Science Foundation of China(No.61976247) the Basic Ability Promotion Project of Guangxi Middle-Aged and Young University Teacher
主 题：Incomplete Multi-view Clustering(IMC) partition space auto-weighted fusion collaborative optimization
摘 要：As a class of effective methods for incomplete multi-view clustering,graph-based algorithms have recently drawn wide ***,most of them could use further improvement regarding the following ***,in some graph-based models,all views are forced to share a common similarity graph regardless of the severe consistency degeneration due to incomplete ***,similarity graph construction and cluster analysis are sometimes performed ***,the contribution difference of individual views is not always carefully *** address these issues simultaneously,this paper proposes an incomplete multi-view clustering algorithm based on auto-weighted fusion in partition *** our algorithm,the information of cluster structure is introduced into the process of similarity learning to construct a desirable similarity graph,information fusion is performed in partition space to alleviate the negative impact brought about by consistency degradation,and all views are adaptively weighted to reflect their different contributions to clustering ***,all the subtasks are collaboratively optimized in a united framework to reach an overall optimal *** results show that the proposed method compares favorably with the state-of-the-art methods.
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