Tensor local linear embedding with global subspace projection optimisation
Abstract In this paper, a novel tensor dimensionality reduction (TDR) approach is proposed, which maintains the local geometric structure of tensor data by tensor local linear embedding and explores the global feature by optimising global subspace projection. Firstly, we analyse the local linear fea...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-04-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cvi2.12083 |
| _version_ | 1818284585413246976 |
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| author | Guo Niu Zhengming Ma |
| author_facet | Guo Niu Zhengming Ma |
| author_sort | Guo Niu |
| collection | DOAJ |
| description | Abstract In this paper, a novel tensor dimensionality reduction (TDR) approach is proposed, which maintains the local geometric structure of tensor data by tensor local linear embedding and explores the global feature by optimising global subspace projection. Firstly, we analyse the local linear feature of tensor data for learning the linear separable embedding of the tenor data. Furthermore, a global subspace projection distance minimisation strategy is introduced to extract the global characteristic of the tensor data. The aim of this strategy is to find an optimal low‐dimensional subspace for TDR. In particular, two novel TDR algorithms are developed by the ensemble of tensor local feature preservation and global subspace projection distance minimisation, which express the subspace projection optimisation as an iteration optimisation problem and a Rayleigh quotient problem, respectively. The extensive experimental results on tensor data classification and clustering have demonstrated the proposed algorithms performed well. |
| first_indexed | 2024-12-13T00:55:09Z |
| format | Article |
| id | doaj.art-bc833ba4e63c4907a0c53e08e4bc3367 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-12-13T00:55:09Z |
| publishDate | 2022-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-bc833ba4e63c4907a0c53e08e4bc33672022-12-22T00:04:50ZengWileyIET Computer Vision1751-96321751-96402022-04-0116324125410.1049/cvi2.12083Tensor local linear embedding with global subspace projection optimisationGuo Niu0Zhengming Ma1School of Electronics and Information Technology Foshan University Foshan ChinaSchool of Electronics and Information Technology Sun Yat‐sen University Guangzhou ChinaAbstract In this paper, a novel tensor dimensionality reduction (TDR) approach is proposed, which maintains the local geometric structure of tensor data by tensor local linear embedding and explores the global feature by optimising global subspace projection. Firstly, we analyse the local linear feature of tensor data for learning the linear separable embedding of the tenor data. Furthermore, a global subspace projection distance minimisation strategy is introduced to extract the global characteristic of the tensor data. The aim of this strategy is to find an optimal low‐dimensional subspace for TDR. In particular, two novel TDR algorithms are developed by the ensemble of tensor local feature preservation and global subspace projection distance minimisation, which express the subspace projection optimisation as an iteration optimisation problem and a Rayleigh quotient problem, respectively. The extensive experimental results on tensor data classification and clustering have demonstrated the proposed algorithms performed well.https://doi.org/10.1049/cvi2.12083local linear embeddingsubspace projectiontensor dimensionality reductiontensors |
| spellingShingle | Guo Niu Zhengming Ma Tensor local linear embedding with global subspace projection optimisation IET Computer Vision local linear embedding subspace projection tensor dimensionality reduction tensors |
| title | Tensor local linear embedding with global subspace projection optimisation |
| title_full | Tensor local linear embedding with global subspace projection optimisation |
| title_fullStr | Tensor local linear embedding with global subspace projection optimisation |
| title_full_unstemmed | Tensor local linear embedding with global subspace projection optimisation |
| title_short | Tensor local linear embedding with global subspace projection optimisation |
| title_sort | tensor local linear embedding with global subspace projection optimisation |
| topic | local linear embedding subspace projection tensor dimensionality reduction tensors |
| url | https://doi.org/10.1049/cvi2.12083 |
| work_keys_str_mv | AT guoniu tensorlocallinearembeddingwithglobalsubspaceprojectionoptimisation AT zhengmingma tensorlocallinearembeddingwithglobalsubspaceprojectionoptimisation |