摘要
针对高维数据冗余性、噪声干扰等问题对多视图子空间聚类性能的影响,提出一种多核低冗余表示学习的稳健多视图子空间聚类方法。首先,通过分析揭示数据在核空间中的冗余性和噪声影响特性,提出采用多核学习来获得局部视图数据的稳健低冗余表示,并利用其替代原始数据实施子空间学习。其次,引入张量分析模型进行多视图融合,从全局角度学习不同视图子空间表示的潜在张量低秩结构,在捕获视图间高阶相关性的同时保持其各异性专属信息。所提方法将稳健低冗余表示学习、视图专属子空间学习以及融合潜在子空间结构学习统一到一个目标函数中,使其在迭代中相互促进。大量实验结果表明,所提方法在多个客观评价指标方面均优于当前主流多视图聚类方法。
Considering the impact of high dimensional data redundancy and noise interference on multiview subspace clustering,a robust multiview subspace clustering method based on multi-kernel low redundancy representation learning was proposed.Firstly,by analyzing and revealing the redundancy and noise influence characteristics of data in kernel space,a multi-kernel learning method was proposed to obtain a robust low-redundancy representation of local view-specific data,which was utilized to replace the original data to implement subspace learning.Secondly,a tensor analysis model was introduced to carry out multiview fusion,so as to learn the potential low-rank tensor structure among different subspace representations from global perspective.It would capture the high-order correlation among views while maintaining their unique information.In this method,robust low-redundancy representation learning,view-specific subspace learning and fusion potential subspace structure learning were unified into the same objective function,so that they could promote each other during iterations.A large number of experimental results demonstrate that the proposed method is superior to the existing mainstream multiview clustering methods on several objective evaluation indicators.
作者
李骜
王卓
于晓洋
陈德运
张英涛
孙广路
LI Ao;WANG Zhuo;YU Xiaoyang;CHEN Deyun;ZHANG Yingtao;SUN Guanglu(School of Computer Science and Technology,Harbin University of Science and Technology,Harbin 150080,China;Postdoctoral Station of Instrument Science and Technology,Harbin University of Science and Technology,Harbin 150080,China;School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China)
出处
《通信学报》
EI
CSCD
北大核心
2021年第11期193-204,共12页
Journal on Communications
基金
国家自然科学基金资助项目(No.62071157)
黑龙江省自然科学基金资助项目(No.YQ2019F011)
黑龙江省青年创新人才计划基金资助项目(No.UNPYSCT-2018203)
黑龙江省高等学校基本科研业务费专项资金资助项目(No.LGYC2018JQ013)
黑龙江省博士后启动基金资助项目(No.LBH-Q19112)。
关键词
低冗余表示学习
子空间聚类
多视图学习
张量分析
low-redundancy representation learning
subspace clustering
multiview learning
tensor analysis
