A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data  

作　　者：Lingyun Xiang Guohan Zhao Qian Li Gwang-Jun Kim Osama Alfarraj Amr Tolba 

机构地区：[1]Changsha Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation,School of Computer and Communication Engineering,Changsha University of Science and Technology,Changsha,410114,China [2]Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems,Changsha University of Science and Technology,Changsha,410114,China [3]Faculty of Engineering and Information Technology,Global Big Data Technologies Centre,University of Technology Sydney,Ultimo,NSW,2007,Australia [4]Department of Computer Engineering,Chonnam National University,Gwangju,61186,Korea [5]Computer Science Department,Community College,King Saud University,Riyadh,11437,Saudi Arabia [6]Department of Mathematics and Computer Science,Faculty of Science,Menoua University,Shebin-El-kom,32511,Egyp

出　　处：《Computers, Materials & Continua》2021年第4期267-284,共18页计算机、材料和连续体（英文）

基　　金：funded by National Natural Science Foundation of China under Grant Nos.61972057 and U1836208;Hunan Provincial Natural Science Foundation of China under Grant No.2019JJ50655;Scientic Research Foundation of Hunan Provincial Education Department of China under Grant No.18B160;Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle Infrastructure Systems(Changsha University of Science and Technology)under Grant No.kfj180402;the“Double First-class”International Cooperation and Development Scientic Research Project of Changsha University of Science and Technology under Grant No.2018IC25;the Researchers Supporting Project No.(RSP-2020/102)King Saud University,Riyadh,Saudi Arabia.

摘　　要：Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled.However,multiple kernel clustering for incomplete data is a critical yet challenging task.Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local optimum.To address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete data.The AMKC method rst clusters the initialized incomplete data.Then,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination coefcients.Finally,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel clustering.The three stages in this process are carried out simultaneously until the convergence condition is met.Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art competitors.Meanwhile,the proposed method gains fast convergence speed.

关 键 词：Multiple kernel clustering absent-kernel imputation incomplete data kernel k-means clustering 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]