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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.14932 |
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| _version_ | 1866908717638746112 |
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| author | Zanco, Daniel Gomes de Pinho Szczecinski, Leszek Benesty, Jacob Kuhn, Eduardo Vinicius |
| author_facet | Zanco, Daniel Gomes de Pinho Szczecinski, Leszek Benesty, Jacob Kuhn, Eduardo Vinicius |
| contents | In this work, we propose a method to efficiently find the regularization parameter for low-rank MMSE filters based on a Kronecker-product representation. We show that the regularization parameter is surprisingly linked to the problem of rank selection and, thus, properly choosing it, is crucial for low-rank settings. The proposed method is validated through simulations, showing significant gains over commonly used methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14932 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Low-rank MMSE filters, Kronecker-product representation, and regularization: a new perspective Zanco, Daniel Gomes de Pinho Szczecinski, Leszek Benesty, Jacob Kuhn, Eduardo Vinicius Machine Learning In this work, we propose a method to efficiently find the regularization parameter for low-rank MMSE filters based on a Kronecker-product representation. We show that the regularization parameter is surprisingly linked to the problem of rank selection and, thus, properly choosing it, is crucial for low-rank settings. The proposed method is validated through simulations, showing significant gains over commonly used methods. |
| title | Low-rank MMSE filters, Kronecker-product representation, and regularization: a new perspective |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.14932 |