Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning
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 inf...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2021-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021217/ |
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| _version_ | 1841539196291907584 |
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| author | Ao LI Zhuo WANG Xiaoyang YU Deyun CHEN Yingtao ZHANG Guanglu SUN |
| author_facet | Ao LI Zhuo WANG Xiaoyang YU Deyun CHEN Yingtao ZHANG Guanglu SUN |
| author_sort | Ao LI |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5e64efa973ce4d3398bb5916a9a70e32 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2021-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-5e64efa973ce4d3398bb5916a9a70e322025-01-14T07:23:11ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-11-014219320459746238Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learningAo LIZhuo WANGXiaoyang YUDeyun CHENYingtao ZHANGGuanglu SUNConsidering 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021217/low-redundancy representation learningsubspace clusteringmultiview learningtensor analysis |
| spellingShingle | Ao LI Zhuo WANG Xiaoyang YU Deyun CHEN Yingtao ZHANG Guanglu SUN Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning Tongxin xuebao low-redundancy representation learning subspace clustering multiview learning tensor analysis |
| title | Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning |
| title_full | Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning |
| title_fullStr | Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning |
| title_full_unstemmed | Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning |
| title_short | Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning |
| title_sort | robust multiview subspace clustering method based on multi kernel low redundancy representation learning |
| topic | low-redundancy representation learning subspace clustering multiview learning tensor analysis |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021217/ |
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