Incomplete multi-view clustering via local and global co-regularization  

作　　者：Jiye LIANG Xiaolin LIU Liang BAI Fuyuan CAO Dianhui WANG 

机构地区：[1]Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education,School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China [2]Artificial Intelligence Research Institute,China University of Mining and Technology,Xuzhou 221116,China [3]Department of Computer Science and Computer Engineering,La Trobe University,Melbourne VIC 3086,Australia

出　　处：《Science China(Information Sciences)》2022年第5期92-107,共16页中国科学（信息科学）（英文版）

基　　金：supported by National Key Research and Development Program of China(Grant No.2020AAA0106100);National Natural Science Foundation of China(Grant Nos.62022052,62006147)。

摘　　要：The incompleteness of multi-view data is a phenomenon associated with real-world data mining applications,which brings a huge challenge for multi-view clustering.Although various types of clustering methods,which try to obtain a complete and consensus clustering result from a latent subspace,have been developed to overcome this problem,most methods excessively rely on views-public instances to bridge the connection with view-private instances.When lacking sufficient views-public instances,existing methods fail to transmit the information among incomplete views effectively.To overcome this limitation,we propose an incomplete multi-view clustering algorithm via local and global co-regularization(IMVC-LG).In this algorithm,we define a new objective function that is composed of two terms:local clustering from each view and global clustering from multiple views,which constrain each other to exploit the local clustering information from different incomplete views and determine a global consensus clustering result,respectively.Furthermore,an iterative optimization method is proposed to minimize the objective function.Finally,we compare the proposed algorithm with other state-of-the-art incomplete multi-view clustering methods on several benchmark datasets to illustrate its effectiveness.

关 键 词：INCOMPLETENESS multi-view clustering local clustering global clustering co-regularization 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]