Low-Rank Tensor Thresholding Ridge Regression
In the area of subspace clustering, methods combining self-representation and spectral clustering are predominant in recent years. For dealing with tensor data, most existing methods vectorize them into vectors and lose most of the spatial information. For removing noise of the data, most existing m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8852636/ |
| _version_ | 1818330302964039680 |
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| author | Kailing Guo Tong Zhang Xiangmin Xu Xiaofen Xing |
| author_facet | Kailing Guo Tong Zhang Xiangmin Xu Xiaofen Xing |
| author_sort | Kailing Guo |
| collection | DOAJ |
| description | In the area of subspace clustering, methods combining self-representation and spectral clustering are predominant in recent years. For dealing with tensor data, most existing methods vectorize them into vectors and lose most of the spatial information. For removing noise of the data, most existing methods focus on the input space and lack consideration of the projection space. Aiming at preserving the spatial information of tensor data, we incorporate tensor mode-d product with low-rank matrices for self-representation. At the same time, we remove noise of the data in both the input space and the projection space, and obtain a robust affinity matrix for spectral clustering. Extensive experiments on several popular subspace clustering datasets show that the proposed method outperforms both traditional subspace clustering methods and recent state-of-the-art deep learning methods. |
| first_indexed | 2024-12-13T13:01:48Z |
| format | Article |
| id | doaj.art-4e13c325dd7a47848bbd6c3894499b6f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:01:48Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4e13c325dd7a47848bbd6c3894499b6f2022-12-21T23:44:58ZengIEEEIEEE Access2169-35362019-01-01715376115377210.1109/ACCESS.2019.29444268852636Low-Rank Tensor Thresholding Ridge RegressionKailing Guo0https://orcid.org/0000-0003-4753-9022Tong Zhang1Xiangmin Xu2Xiaofen Xing3South China University of Technology, Guangzhou, ChinaSouth China University of Technology, Guangzhou, ChinaSouth China University of Technology, Guangzhou, ChinaSouth China University of Technology, Guangzhou, ChinaIn the area of subspace clustering, methods combining self-representation and spectral clustering are predominant in recent years. For dealing with tensor data, most existing methods vectorize them into vectors and lose most of the spatial information. For removing noise of the data, most existing methods focus on the input space and lack consideration of the projection space. Aiming at preserving the spatial information of tensor data, we incorporate tensor mode-d product with low-rank matrices for self-representation. At the same time, we remove noise of the data in both the input space and the projection space, and obtain a robust affinity matrix for spectral clustering. Extensive experiments on several popular subspace clustering datasets show that the proposed method outperforms both traditional subspace clustering methods and recent state-of-the-art deep learning methods.https://ieeexplore.ieee.org/document/8852636/Tensorlow-ranksubspace clustering |
| spellingShingle | Kailing Guo Tong Zhang Xiangmin Xu Xiaofen Xing Low-Rank Tensor Thresholding Ridge Regression IEEE Access Tensor low-rank subspace clustering |
| title | Low-Rank Tensor Thresholding Ridge Regression |
| title_full | Low-Rank Tensor Thresholding Ridge Regression |
| title_fullStr | Low-Rank Tensor Thresholding Ridge Regression |
| title_full_unstemmed | Low-Rank Tensor Thresholding Ridge Regression |
| title_short | Low-Rank Tensor Thresholding Ridge Regression |
| title_sort | low rank tensor thresholding ridge regression |
| topic | Tensor low-rank subspace clustering |
| url | https://ieeexplore.ieee.org/document/8852636/ |
| work_keys_str_mv | AT kailingguo lowranktensorthresholdingridgeregression AT tongzhang lowranktensorthresholdingridgeregression AT xiangminxu lowranktensorthresholdingridgeregression AT xiaofenxing lowranktensorthresholdingridgeregression |