Randomized block Krylov method for approximation of truncated tensor SVD
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914415227437056 |
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| author | Kooshkghazi, Malihe Nobakht Ahmadi-Asl, Salman de Almeida, Andre L. F. |
| author_facet | Kooshkghazi, Malihe Nobakht Ahmadi-Asl, Salman de Almeida, Andre L. F. |
| contents | This paper is devoted to studying the application of the block Krylov subspace method for approximation of the truncated tensor SVD (T-SVD). The theoretical results of the proposed randomized approach are presented. Several experimental experiments using synthetics and real-world data are conducted to verify the efficiency and feasibility of the proposed randomized approach, and the numerical results show that the proposed method provides promising results. Applications of the proposed approach to data completion and data compression are presented. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04989 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Randomized block Krylov method for approximation of truncated tensor SVD Kooshkghazi, Malihe Nobakht Ahmadi-Asl, Salman de Almeida, Andre L. F. Numerical Analysis This paper is devoted to studying the application of the block Krylov subspace method for approximation of the truncated tensor SVD (T-SVD). The theoretical results of the proposed randomized approach are presented. Several experimental experiments using synthetics and real-world data are conducted to verify the efficiency and feasibility of the proposed randomized approach, and the numerical results show that the proposed method provides promising results. Applications of the proposed approach to data completion and data compression are presented. |
| title | Randomized block Krylov method for approximation of truncated tensor SVD |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2504.04989 |