A tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction
Tensor compression algorithms play an important role in the processing of multidimensional signals. In previous work, tensor data structures are usually destroyed by vectorization operations, resulting in information loss and new noise. To this end, this article proposes a tensor compression algorit...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-04-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147720916408 |
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| _version_ | 1832553223041318912 |
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| author | Chenquan Gan Junwei Mao Zufan Zhang Qingyi Zhu |
| author_facet | Chenquan Gan Junwei Mao Zufan Zhang Qingyi Zhu |
| author_sort | Chenquan Gan |
| collection | DOAJ |
| description | Tensor compression algorithms play an important role in the processing of multidimensional signals. In previous work, tensor data structures are usually destroyed by vectorization operations, resulting in information loss and new noise. To this end, this article proposes a tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction, which mainly includes three parts: tensor dictionary representation, dictionary preprocessing, and dictionary update. Specifically, the tensor is respectively performed by the sparse representation and Tucker decomposition, from which one can obtain the dictionary, sparse coefficient, and core tensor. Furthermore, the sparse representation can be obtained through the relationship between sparse coefficient and core tensor. In addition, the dimensionality of the input tensor is reduced by using the concentrated dictionary learning. Finally, some experiments show that, compared with other algorithms, the proposed algorithm has obvious advantages in preserving the original data information and denoising ability. |
| format | Article |
| id | doaj-art-c27dfad8f6104db48c165ca6765d242f |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2020-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-c27dfad8f6104db48c165ca6765d242f2025-02-03T05:54:33ZengWileyInternational Journal of Distributed Sensor Networks1550-14772020-04-011610.1177/1550147720916408A tensor compression algorithm using Tucker decomposition and dictionary dimensionality reductionChenquan Gan0Junwei Mao1Zufan Zhang2Qingyi Zhu3Engineering Research Center of Mobile Communications, Ministry of Education, Chongqing, ChinaEngineering Research Center of Mobile Communications, Ministry of Education, Chongqing, ChinaEngineering Research Center of Mobile Communications, Ministry of Education, Chongqing, ChinaSchool of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing, ChinaTensor compression algorithms play an important role in the processing of multidimensional signals. In previous work, tensor data structures are usually destroyed by vectorization operations, resulting in information loss and new noise. To this end, this article proposes a tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction, which mainly includes three parts: tensor dictionary representation, dictionary preprocessing, and dictionary update. Specifically, the tensor is respectively performed by the sparse representation and Tucker decomposition, from which one can obtain the dictionary, sparse coefficient, and core tensor. Furthermore, the sparse representation can be obtained through the relationship between sparse coefficient and core tensor. In addition, the dimensionality of the input tensor is reduced by using the concentrated dictionary learning. Finally, some experiments show that, compared with other algorithms, the proposed algorithm has obvious advantages in preserving the original data information and denoising ability.https://doi.org/10.1177/1550147720916408 |
| spellingShingle | Chenquan Gan Junwei Mao Zufan Zhang Qingyi Zhu A tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction International Journal of Distributed Sensor Networks |
| title | A tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction |
| title_full | A tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction |
| title_fullStr | A tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction |
| title_full_unstemmed | A tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction |
| title_short | A tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction |
| title_sort | tensor compression algorithm using tucker decomposition and dictionary dimensionality reduction |
| url | https://doi.org/10.1177/1550147720916408 |
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