Sufficient recovery conditions for noise-buried low rank tensors
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910743622844416 |
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| author | Petrov, Sergey Zamarashkin, Nikolai |
| author_facet | Petrov, Sergey Zamarashkin, Nikolai |
| contents | Low-rank tensor approximation error bounds are proposed for the case of noisy input data that depend on low-rank representation type, rank and the dimensionality of the tensor. The bounds show that high-dimensional low-rank structured approximations provide superior noise-filtering properties compared to matrices with the same rank and total element count. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_02088 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Sufficient recovery conditions for noise-buried low rank tensors Petrov, Sergey Zamarashkin, Nikolai Numerical Analysis Low-rank tensor approximation error bounds are proposed for the case of noisy input data that depend on low-rank representation type, rank and the dimensionality of the tensor. The bounds show that high-dimensional low-rank structured approximations provide superior noise-filtering properties compared to matrices with the same rank and total element count. |
| title | Sufficient recovery conditions for noise-buried low rank tensors |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2312.02088 |