作者：
Liu, Xinwang;Li, Miaomiao;Tang, Chang;Xia, Jingyuan;Xiong, Jian;...
通讯作者：
Liu, X.
作者机构：
School of Business Administration, Southwestern University of Finance and Economics, Chengdu, Sichuan, China
Department of Electric and Electronic Engineering, Imperial College London, London, U.K
College of System Engineering, National University of Defense Technology, Changsha, Hunan, China
Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
School of Computer Science, China University of Geosciences, Wuhan, Hubei, China
College of Computer, National University of Defense Technology, Changsha, Hunan, China
Department of Computer, Changsha College, Changsha, Hunan, China
Department of Computer Science, Technische Universität Kaiserslautern, 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 China (Grant Number: 61773392, 61922088 and 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...
