Orthogonal random projection for tensor completion
The low‐rank tensor completion problem, which aims to recover the missing data from partially observable data. However, most of the existing tensor completion algorithms based on Tucker decomposition cannot avoid using singular value decomposition (SVD) operation to calculate the Tucker factors, so...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-08-01
|
| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2018.5764 |
| _version_ | 1797684736868483072 |
|---|---|
| author | Yali Feng Guoxu Zhou |
| author_facet | Yali Feng Guoxu Zhou |
| author_sort | Yali Feng |
| collection | DOAJ |
| description | The low‐rank tensor completion problem, which aims to recover the missing data from partially observable data. However, most of the existing tensor completion algorithms based on Tucker decomposition cannot avoid using singular value decomposition (SVD) operation to calculate the Tucker factors, so they are not suitable for the completion of large‐scale data. To solve this problem, they propose a new faster tensor completion algorithm, which uses the method of random projection to project the unfolding matrix of each mode of the tensor into the low‐dimensional subspace, and then obtain the Tucker factors by the orthogonal decomposition. Their method can effectively avoid the high computational cost of SVD operation. The results of the synthetic data experiments and real data experiments verify the effectiveness and feasibility of their method. |
| first_indexed | 2024-03-12T00:34:07Z |
| format | Article |
| id | doaj.art-64334701555d46e88369fb52d887b142 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:34:07Z |
| publishDate | 2020-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-64334701555d46e88369fb52d887b1422023-09-15T10:06:16ZengWileyIET Computer Vision1751-96321751-96402020-08-0114523324010.1049/iet-cvi.2018.5764Orthogonal random projection for tensor completionYali Feng0Guoxu Zhou1School of AutomationGuangdong University of TechnologyGuangzhouPeople's Republic of ChinaSchool of AutomationGuangdong University of TechnologyGuangzhouPeople's Republic of ChinaThe low‐rank tensor completion problem, which aims to recover the missing data from partially observable data. However, most of the existing tensor completion algorithms based on Tucker decomposition cannot avoid using singular value decomposition (SVD) operation to calculate the Tucker factors, so they are not suitable for the completion of large‐scale data. To solve this problem, they propose a new faster tensor completion algorithm, which uses the method of random projection to project the unfolding matrix of each mode of the tensor into the low‐dimensional subspace, and then obtain the Tucker factors by the orthogonal decomposition. Their method can effectively avoid the high computational cost of SVD operation. The results of the synthetic data experiments and real data experiments verify the effectiveness and feasibility of their method.https://doi.org/10.1049/iet-cvi.2018.5764orthogonal random projectionlow-rank tensor completion problemmissing datapartially observable dataTucker decompositionsingular value decomposition operation |
| spellingShingle | Yali Feng Guoxu Zhou Orthogonal random projection for tensor completion IET Computer Vision orthogonal random projection low-rank tensor completion problem missing data partially observable data Tucker decomposition singular value decomposition operation |
| title | Orthogonal random projection for tensor completion |
| title_full | Orthogonal random projection for tensor completion |
| title_fullStr | Orthogonal random projection for tensor completion |
| title_full_unstemmed | Orthogonal random projection for tensor completion |
| title_short | Orthogonal random projection for tensor completion |
| title_sort | orthogonal random projection for tensor completion |
| topic | orthogonal random projection low-rank tensor completion problem missing data partially observable data Tucker decomposition singular value decomposition operation |
| url | https://doi.org/10.1049/iet-cvi.2018.5764 |
| work_keys_str_mv | AT yalifeng orthogonalrandomprojectionfortensorcompletion AT guoxuzhou orthogonalrandomprojectionfortensorcompletion |