Automatic Regularization for Linear MMSE Filters
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913406872715264 |
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| author | Zanco, Daniel Gomes de Pinho Szczecinski, Leszek Benesty, Jacob |
| author_facet | Zanco, Daniel Gomes de Pinho Szczecinski, Leszek Benesty, Jacob |
| contents | In this work, we consider the problem of regularization in the design of minimum mean square error (MMSE) linear filters. Using the relationship with statistical machine learning methods, using a Bayesian approach, the regularization parameter is found from the observed signals in a simple and automatic manner. The proposed approach is illustrated in system identification and beamforming examples, where the automatic regularization is shown to yield near-optimal results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_06560 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Automatic Regularization for Linear MMSE Filters Zanco, Daniel Gomes de Pinho Szczecinski, Leszek Benesty, Jacob Information Theory Machine Learning Signal Processing In this work, we consider the problem of regularization in the design of minimum mean square error (MMSE) linear filters. Using the relationship with statistical machine learning methods, using a Bayesian approach, the regularization parameter is found from the observed signals in a simple and automatic manner. The proposed approach is illustrated in system identification and beamforming examples, where the automatic regularization is shown to yield near-optimal results. |
| title | Automatic Regularization for Linear MMSE Filters |
| topic | Information Theory Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2312.06560 |