多核低冗余表示学习的稳健多视图子空间聚类方法

李骜; 王卓; 于晓洋; 陈德运; 张英涛; 孙广路

doi:10.11959/j.issn.1000-436x.2021217

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多核低冗余表示学习的稳健多视图子空间聚类方法

学术论文 | 更新时间：2024-06-05

- 多核低冗余表示学习的稳健多视图子空间聚类方法
- Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning
- 通信学报 2021年42卷第11期页码：193-204
- 作者机构：
  
  1. 哈尔滨理工大学计算机科学与技术学院，黑龙江哈尔滨 150080
  2. 哈尔滨理工大学仪器科学与技术博士后流动站，黑龙江哈尔滨 150080
  3. 哈尔滨工业大学计算机科学与技术学院，黑龙江哈尔滨 150001
- 作者简介：
  
  [ "李骜（1986− ），男，黑龙江哈尔滨人，博士，哈尔滨理工大学副教授、博士生导师，主要研究方向为模式识别、机器学习等" ]
  [ "王卓（1997− ），女，黑龙江牡丹江人，哈尔滨理工大学硕士生，主要研究方向为数据挖掘、机器学习" ]
  [ "于晓洋（1962− ），男，黑龙江哈尔滨人，博士，哈尔滨理工大学教授、博士生导师，主要研究方向为三维视觉检测、图像处理等" ]
  [ "陈德运（1962− ），男，黑龙江哈尔滨人，博士，哈尔滨理工大学教授、博士生导师，主要研究方向为探测与成像技术、图像处理等" ]
  [ "张英涛（1975− ），女，黑龙江哈尔滨人，博士，哈尔滨工业大学副教授，主要研究方向为人工智能与信息处理等" ]
  [ "孙广路（1979− ），男，黑龙江哈尔滨人，博士，哈尔滨理工大学教授、博士生导师，主要研究方向为机器学习、网络安全等" ]
- 基金信息：
  
  国家自然科学基金资助项目(62071157);黑龙江省自然科学基金资助项目(YQ2019F011);黑龙江省青年创新人才计划基金资助项目(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(LGYC2018JQ013);黑龙江省博士后启动基金资助项目(LBH-Q19112)
- DOI：10.11959/j.issn.1000-436x.2021217
  中图分类号： TP391
- 网络出版日期：2021-11，
  
  纸质出版日期：2021-11-25
- 稿件说明：
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李骜, 王卓, 于晓洋, 等. 多核低冗余表示学习的稳健多视图子空间聚类方法[J]. 通信学报, 2021,42(11):193-204.

Ao LI, Zhuo WANG, Xiaoyang YU, et al. Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning[J]. Journal on communications, 2021, 42(11): 193-204.
李骜, 王卓, 于晓洋, 等. 多核低冗余表示学习的稳健多视图子空间聚类方法[J]. 通信学报, 2021,42(11):193-204. DOI： 10.11959/j.issn.1000-436x.2021217.

Ao LI, Zhuo WANG, Xiaoyang YU, et al. Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning[J]. Journal on communications, 2021, 42(11): 193-204. DOI： 10.11959/j.issn.1000-436x.2021217.

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。

Abstract

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.

关键词

Keywords

references

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