Tensorial Multi-Linear Multi-View Clustering via Schatten-p Norm
Despite satisfactory clustering performance, current subspace-based multi-view clustering methods still suffer from the following limitations. 1) They usually concentrate on the data features in linear subspaces and ignore the features in nonlinear subspaces. 2) They treat all singular values equall...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10035394/ |
| _version_ | 1811169688374214656 |
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| author | Wenzhe Liu Li Jiang Da Liu Yong Zhang |
| author_facet | Wenzhe Liu Li Jiang Da Liu Yong Zhang |
| author_sort | Wenzhe Liu |
| collection | DOAJ |
| description | Despite satisfactory clustering performance, current subspace-based multi-view clustering methods still suffer from the following limitations. 1) They usually concentrate on the data features in linear subspaces and ignore the features in nonlinear subspaces. 2) They treat all singular values equally without considering their different contribution degrees, leading to suboptimal problems. Based on the above considerations, we propose tensorial multi-linear multi-view clustering via the weighted Schatten-p norm, named TM2vC. TM2vC integrates high-order relationships learning and latent structure learning of multi-view data into one framework. We apply third-order tensors stacked by low-dimensional representations to capture high-order relationships among multivariate data and the weighted Schatten-p norm to distinguish different singular values. Additionally, we employ hypergraph constraints to conserve high-order local geometric structures in the high-dimensional subspace. Comprehensive experiments on diverse datasets verify the effectiveness and superiority of the proposed TM2vC on six indicators. |
| first_indexed | 2024-04-10T16:46:23Z |
| format | Article |
| id | doaj.art-47570c560e7846378ebb3038f608828d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T16:46:23Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-47570c560e7846378ebb3038f608828d2023-02-08T00:00:55ZengIEEEIEEE Access2169-35362023-01-0111111321114210.1109/ACCESS.2023.324181010035394Tensorial Multi-Linear Multi-View Clustering via Schatten-p NormWenzhe Liu0https://orcid.org/0000-0002-6290-8941Li Jiang1Da Liu2Yong Zhang3https://orcid.org/0000-0003-1024-5741School of Computer Science and Technology, Dalian University of Technology, Dalian, ChinaSchool of Information Engineering, Huzhou University, Huzhou, ChinaSchool of Information Engineering, Huzhou University, Huzhou, ChinaSchool of Information Engineering, Huzhou University, Huzhou, ChinaDespite satisfactory clustering performance, current subspace-based multi-view clustering methods still suffer from the following limitations. 1) They usually concentrate on the data features in linear subspaces and ignore the features in nonlinear subspaces. 2) They treat all singular values equally without considering their different contribution degrees, leading to suboptimal problems. Based on the above considerations, we propose tensorial multi-linear multi-view clustering via the weighted Schatten-p norm, named TM2vC. TM2vC integrates high-order relationships learning and latent structure learning of multi-view data into one framework. We apply third-order tensors stacked by low-dimensional representations to capture high-order relationships among multivariate data and the weighted Schatten-p norm to distinguish different singular values. Additionally, we employ hypergraph constraints to conserve high-order local geometric structures in the high-dimensional subspace. Comprehensive experiments on diverse datasets verify the effectiveness and superiority of the proposed TM2vC on six indicators.https://ieeexplore.ieee.org/document/10035394/Multi-linear multi-viewweighted Schatten-p normclustering |
| spellingShingle | Wenzhe Liu Li Jiang Da Liu Yong Zhang Tensorial Multi-Linear Multi-View Clustering via Schatten-p Norm IEEE Access Multi-linear multi-view weighted Schatten-p norm clustering |
| title | Tensorial Multi-Linear Multi-View Clustering via Schatten-p Norm |
| title_full | Tensorial Multi-Linear Multi-View Clustering via Schatten-p Norm |
| title_fullStr | Tensorial Multi-Linear Multi-View Clustering via Schatten-p Norm |
| title_full_unstemmed | Tensorial Multi-Linear Multi-View Clustering via Schatten-p Norm |
| title_short | Tensorial Multi-Linear Multi-View Clustering via Schatten-p Norm |
| title_sort | tensorial multi linear multi view clustering via schatten p norm |
| topic | Multi-linear multi-view weighted Schatten-p norm clustering |
| url | https://ieeexplore.ieee.org/document/10035394/ |
| work_keys_str_mv | AT wenzheliu tensorialmultilinearmultiviewclusteringviaschattenpnorm AT lijiang tensorialmultilinearmultiviewclusteringviaschattenpnorm AT daliu tensorialmultilinearmultiviewclusteringviaschattenpnorm AT yongzhang tensorialmultilinearmultiviewclusteringviaschattenpnorm |