Moment Constraints and Phase Recovery for Multireference Alignment
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911239662206976 |
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| author | Shahverdi, Vahid Ström, Emanuel Andén, Joakim |
| author_facet | Shahverdi, Vahid Ström, Emanuel Andén, Joakim |
| contents | Multireference alignment (MRA) refers to the problem of recovering a signal from noisy samples subject to random circular shifts. Expectation--maximization (EM) and variational approaches use statistical modeling to achieve high accuracy at the cost of solving computationally expensive optimization problems. The method of moments, instead, achieves fast reconstructions by utilizing the power spectrum and bispectrum to determine the signal up to shift. Our approach combines the two philosophies by viewing the power spectrum as a manifold on which to constrain the signal. We then maximize the data likelihood function on this manifold with a gradient-based approach to estimate the true signal. Algorithmically, our method involves iterating between template alignment and projections onto the manifold. The method offers increased speed compared to EM and demonstrates improved accuracy over bispectrum-based methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_04868 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Moment Constraints and Phase Recovery for Multireference Alignment Shahverdi, Vahid Ström, Emanuel Andén, Joakim Information Theory 94A12, 92C55, 62F12, 68U10, 90C30, 58C25, 58E05 Multireference alignment (MRA) refers to the problem of recovering a signal from noisy samples subject to random circular shifts. Expectation--maximization (EM) and variational approaches use statistical modeling to achieve high accuracy at the cost of solving computationally expensive optimization problems. The method of moments, instead, achieves fast reconstructions by utilizing the power spectrum and bispectrum to determine the signal up to shift. Our approach combines the two philosophies by viewing the power spectrum as a manifold on which to constrain the signal. We then maximize the data likelihood function on this manifold with a gradient-based approach to estimate the true signal. Algorithmically, our method involves iterating between template alignment and projections onto the manifold. The method offers increased speed compared to EM and demonstrates improved accuracy over bispectrum-based methods. |
| title | Moment Constraints and Phase Recovery for Multireference Alignment |
| topic | Information Theory 94A12, 92C55, 62F12, 68U10, 90C30, 58C25, 58E05 |
| url | https://arxiv.org/abs/2409.04868 |