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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2207.05802 |
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| _version_ | 1866917586442125312 |
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| author | Yang, Yuepeng Ma, Cong |
| author_facet | Yang, Yuepeng Ma, Cong |
| contents | This paper is concerned with noisy matrix completion--the problem of recovering a low-rank matrix from partial and noisy entries. Under uniform sampling and incoherence assumptions, we prove that a tuning-free square-root matrix completion estimator (square-root MC) achieves optimal statistical performance for solving the noisy matrix completion problem. Similar to the square-root Lasso estimator in high-dimensional linear regression, square-root MC does not rely on the knowledge of the size of the noise. While solving square-root MC is a convex program, our statistical analysis of square-root MC hinges on its intimate connections to a nonconvex rank-constrained estimator. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_05802 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Optimal tuning-free convex relaxation for noisy matrix completion Yang, Yuepeng Ma, Cong Statistics Theory Information Theory This paper is concerned with noisy matrix completion--the problem of recovering a low-rank matrix from partial and noisy entries. Under uniform sampling and incoherence assumptions, we prove that a tuning-free square-root matrix completion estimator (square-root MC) achieves optimal statistical performance for solving the noisy matrix completion problem. Similar to the square-root Lasso estimator in high-dimensional linear regression, square-root MC does not rely on the knowledge of the size of the noise. While solving square-root MC is a convex program, our statistical analysis of square-root MC hinges on its intimate connections to a nonconvex rank-constrained estimator. |
| title | Optimal tuning-free convex relaxation for noisy matrix completion |
| topic | Statistics Theory Information Theory |
| url | https://arxiv.org/abs/2207.05802 |