Multiview clustering method for view-unaligned data
A new challenge for multi-view learning was posed by corrupted view-correspondences.To address this issue， an effective multi-view learning method for view-unaligned data was proposed.First，to capture cross-view latent affinity in multi-view heterogenous feature spaces，representation learning was em...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2022-07-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022134/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841539953080664064 |
|---|---|
| author | Ao LI Cong FENG Yutong NIU Shibiao XU Yingtao ZHANG Guanglu SUN |
| author_facet | Ao LI Cong FENG Yutong NIU Shibiao XU Yingtao ZHANG Guanglu SUN |
| author_sort | Ao LI |
| collection | DOAJ |
| description | A new challenge for multi-view learning was posed by corrupted view-correspondences.To address this issue， an effective multi-view learning method for view-unaligned data was proposed.First，to capture cross-view latent affinity in multi-view heterogenous feature spaces，representation learning was employed based on multi-view non-negative matrix factorization to embed original features into a measurable low-dimensional subspace.Second, view-alignment relationships were modeled as optimal matching of a bipartite graph, which could be generalized to multiple-views situations via the proposed concept reference view.Representation learning and data alignment were further integrated into a unified Bi-level optimization framework to mutually boost the two learning processes, effectively enhancing the ability to learn from view-unaligned data.Extensive experimental results of view-unaligned clustering on three public datasets demonstrate that the proposed method outperforms eight advanced multiview clustering methods on multiple evaluation metrics. |
| format | Article |
| id | doaj-art-05071d622730438d83d738b83a7fbcb8 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2022-07-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-05071d622730438d83d738b83a7fbcb82025-01-14T06:29:44ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2022-07-014314315259395090Multiview clustering method for view-unaligned dataAo LICong FENGYutong NIUShibiao XUYingtao ZHANGGuanglu SUNA new challenge for multi-view learning was posed by corrupted view-correspondences.To address this issue， an effective multi-view learning method for view-unaligned data was proposed.First，to capture cross-view latent affinity in multi-view heterogenous feature spaces，representation learning was employed based on multi-view non-negative matrix factorization to embed original features into a measurable low-dimensional subspace.Second, view-alignment relationships were modeled as optimal matching of a bipartite graph, which could be generalized to multiple-views situations via the proposed concept reference view.Representation learning and data alignment were further integrated into a unified Bi-level optimization framework to mutually boost the two learning processes, effectively enhancing the ability to learn from view-unaligned data.Extensive experimental results of view-unaligned clustering on three public datasets demonstrate that the proposed method outperforms eight advanced multiview clustering methods on multiple evaluation metrics.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022134/clustering analysismultiview learningview-unaligned datanon-negative matrix factorization |
| spellingShingle | Ao LI Cong FENG Yutong NIU Shibiao XU Yingtao ZHANG Guanglu SUN Multiview clustering method for view-unaligned data Tongxin xuebao clustering analysis multiview learning view-unaligned data non-negative matrix factorization |
| title | Multiview clustering method for view-unaligned data |
| title_full | Multiview clustering method for view-unaligned data |
| title_fullStr | Multiview clustering method for view-unaligned data |
| title_full_unstemmed | Multiview clustering method for view-unaligned data |
| title_short | Multiview clustering method for view-unaligned data |
| title_sort | multiview clustering method for view unaligned data |
| topic | clustering analysis multiview learning view-unaligned data non-negative matrix factorization |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022134/ |
| work_keys_str_mv | AT aoli multiviewclusteringmethodforviewunaligneddata AT congfeng multiviewclusteringmethodforviewunaligneddata AT yutongniu multiviewclusteringmethodforviewunaligneddata AT shibiaoxu multiviewclusteringmethodforviewunaligneddata AT yingtaozhang multiviewclusteringmethodforviewunaligneddata AT guanglusun multiviewclusteringmethodforviewunaligneddata |