[1]张福康,尹宏伟,成新民,等.联合低秩表示和稀疏约束的双层多视角子空间聚类[J].南京师范大学学报(工程技术版),2022,(01):059-67.[doi:10.3969/j.issn.1672-1292.2022.01.009]
 Zhang Fukang,Yin Hongwei,Cheng Xinmin,et al.Double-layer Multi-view Subspace Clustering with Joint Low-rankRepresentation and Sparsity Constraint[J].Journal of Nanjing Normal University(Engineering and Technology),2022,(01):059-67.[doi:10.3969/j.issn.1672-1292.2022.01.009]
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联合低秩表示和稀疏约束的双层多视角子空间聚类
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2022年01期
页码:
059-67
栏目:
机器学习
出版日期:
2022-03-15

文章信息/Info

Title:
Double-layer Multi-view Subspace Clustering with Joint Low-rankRepresentation and Sparsity Constraint
文章编号:
1672-1292(2022)01-0059-09
作者:
张福康1尹宏伟1成新民1杜文辉2徐黄镇2
(1.湖州师范学院信息工程学院,浙江 湖州 313000)(2.湖州市特种设备检测研究院,浙江 湖州 313000)
Author(s):
Zhang Fukang1Yin Hongwei1Cheng Xinmin1Du Wenhui2Xu Huangzhen2
(1.School of Information Engineering,Huzhou University,Huzhou 313000,China)(2.Huzhou Special Equipment Inspection Center,Huzhou 313000,China)
关键词:
多视角聚类多视角子空间聚类低秩表示稀疏约束
Keywords:
multi-view clusteringmulti-view subspace clusteringlow-rank representationsparse constraint
分类号:
TP391
DOI:
10.3969/j.issn.1672-1292.2022.01.009
文献标志码:
A
摘要:
多视图子空间聚类是处理高维数据的一种聚类方法,通过分别在每个视图上构造邻接矩阵的方法解决聚类问题,但未考虑到低秩表示和稀疏约束的结合在构造邻接矩阵中的重要性. 针对此问题,提出一种联合低秩表示和稀疏约束的双层多视角子空间聚类方法,使其更全面地描述数据本身,从而实现更有效的聚类,并采用ADMM方法来解决每个视图相关的低秩表示和稀疏性约束优化问题. 在多个数据集上的实验表明,其聚类性能比现有的多视角子空间聚类算法好,低秩表示和稀疏约束的结合可以提高聚类的准确性.
Abstract:
Multi-view clustering is the use of multiple different description of the data set to the same class of similar data together as far as possible. Among them,multi-view subspace clustering is a kind of clustering method to deal with high-dimensional data. The current approach is to solve the multi-view subspace clustering problem by constructing the affinity matrix on each view separately. The importance of the combination of low-rank representation and sparse constraints in constructing the adjacency matrix is not considered. In order to solve this problem,this paper proposes a double-layer multi-view subspace clustering with joint low-rank representation and sparsity constraint,so that it can describe the data itself more comprehensively,so as to achieve more effective clustering. For the low-rank representation and sparsity constraint optimization problems related to each view,we use ADMM method to solve them. Finally,experiments are conducted on multiple data sets,and it is found that the clustering performance of the proposed algorithm is better than the existing multi-view subspace clustering algorithm. It is proved that the combination of low rank representation and sparse constraint can improve the accuracy of clustering.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2021-08-31.
基金项目:湖州市公益性应用研究项目(2021GZ05)、湖州市特种设备检测研究院委托开发项目(073-20201210-02).
通讯作者:尹宏伟,博士,讲师,研究方向:机器学习与模式识别. E-mail:02713@zjhu.edu.cn
更新日期/Last Update: 2022-03-15