Automatic selection of hyper-parameters via the use of softened profile likelihood
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916026701053952 |
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| author | Chen, Gengyang Zhu, Mu |
| author_facet | Chen, Gengyang Zhu, Mu |
| contents | We extend a heuristic method for automatic dimensionality selection, which maximizes a profile likelihood to identify "elbows" in scree plots. Our extension enables researchers to make automatic choices of multiple hyper-parameters simultaneously. To facilitate our extension to multi-dimensions, we propose a "softened" profile likelihood. We present two distinct parameterizations of our solution and demonstrate our approach on elastic nets, support vector machines, and neural networks. We also report a small simulation study to investigate violations to an assumption we make, and briefly discuss applications of our method to other data-analytic tasks than hyper-parameter selection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25632 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic selection of hyper-parameters via the use of softened profile likelihood Chen, Gengyang Zhu, Mu Methodology We extend a heuristic method for automatic dimensionality selection, which maximizes a profile likelihood to identify "elbows" in scree plots. Our extension enables researchers to make automatic choices of multiple hyper-parameters simultaneously. To facilitate our extension to multi-dimensions, we propose a "softened" profile likelihood. We present two distinct parameterizations of our solution and demonstrate our approach on elastic nets, support vector machines, and neural networks. We also report a small simulation study to investigate violations to an assumption we make, and briefly discuss applications of our method to other data-analytic tasks than hyper-parameter selection. |
| title | Automatic selection of hyper-parameters via the use of softened profile likelihood |
| topic | Methodology |
| url | https://arxiv.org/abs/2510.25632 |