作者：
Liu, Xinwang;Li, Miaomiao;Tang, Chang;Xia, Jingyuan;Xiong, Jian;...
通讯作者：
Liu, X.
作者机构：
Natl Univ Def Technol, Coll Comp, Changsha 410073, Hunan, Peoples R China.
Changsha Coll, Dept Comp, Changsha 410073, Hunan, Peoples R China.
China Univ Geosci, Sch Comp Sci, Wuhan 430074, Hubei, Peoples R China.
Imperial Coll London, Dept Elect & Elect Engn, London SW7 2AZ, England.
Southwestern Univ Finance & Econ, Sch Business Adm, Chengdu 611130, Sichuan, Peoples R China.
Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Hunan, Peoples R China.
Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland.
Tech Univ Kaiserslautern, Dept Comp Sci, D-67653 Kaiserslautern, Germany.
通讯机构：
College of Computer, Hunan, China
语种：
英文
关键词：
Multiple kernel clustering,multiple view learning,incomplete kernel learning
期刊：
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN：
0162-8828
年：
2021
卷：
43
期：
8
页码：
2634-2646
基金类别：
Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61773392, 61922088, 61701451
摘要：
Incomplete multi-view clustering (IMVC) optimally combines multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) forms a benchmark, which redefines IMVC as a joint optimization problem where the clustering and kernel matrix imputation tasks are alternately performed until convergence. Though demonstrating promising performance in various applications, we observe that the manner of kernel matrix imputation in MKKM-IK would incur intensive computational and storage complexities, over-complicated optimization and limitedly improved clusterin...
