Towards a pretrained deep learning estimator of the Linfoot informational correlation
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915673218744320 |
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| author | Berg, Stéphanie M. van den Halekoh, Ulrich Möller, Sören Jensen, Andreas Kryger Hjelmborg, Jacob von Bornemann |
| author_facet | Berg, Stéphanie M. van den Halekoh, Ulrich Möller, Sören Jensen, Andreas Kryger Hjelmborg, Jacob von Bornemann |
| contents | We develop a supervised deep-learning approach to estimate mutual information between two continuous random variables. As labels, we use the Linfoot informational correlation, a transformation of mutual information that has many important properties. Our method is based on ground truth labels for Gaussian and Clayton copulas. We compare our method with estimators based on kernel density, k-nearest neighbours and neural estimators. We show generally lower bias and lower variance. As a proof of principle, future research could look into training the model with a more diverse set of examples from other copulas for which ground truth labels are available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12358 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards a pretrained deep learning estimator of the Linfoot informational correlation Berg, Stéphanie M. van den Halekoh, Ulrich Möller, Sören Jensen, Andreas Kryger Hjelmborg, Jacob von Bornemann Machine Learning Methodology We develop a supervised deep-learning approach to estimate mutual information between two continuous random variables. As labels, we use the Linfoot informational correlation, a transformation of mutual information that has many important properties. Our method is based on ground truth labels for Gaussian and Clayton copulas. We compare our method with estimators based on kernel density, k-nearest neighbours and neural estimators. We show generally lower bias and lower variance. As a proof of principle, future research could look into training the model with a more diverse set of examples from other copulas for which ground truth labels are available. |
| title | Towards a pretrained deep learning estimator of the Linfoot informational correlation |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2512.12358 |