Feature Understanding and Sparsity Enhancement via 2-Layered kernel machines (2L-FUSE)
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912578867822592 |
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| author | Camattari, Fabiana Guastavino, Sabrina Marchetti, Francesco Perracchione, Emma |
| author_facet | Camattari, Fabiana Guastavino, Sabrina Marchetti, Francesco Perracchione, Emma |
| contents | We propose a novel sparsity enhancement strategy for regression tasks, based on learning a data-adaptive kernel metric, i.e., a shape matrix, through 2-Layered kernel machines. The resulting shape matrix, which defines a Mahalanobis-type deformation of the input space, is then factorized via an eigen-decomposition, allowing us to identify the most informative directions in the space of features. This data-driven approach provides a flexible, interpretable and accurate feature reduction scheme. Numerical experiments on synthetic and applications to real datasets of geomagnetic storms demonstrate that our approach achieves minimal yet highly informative feature sets without losing predictive performance. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_07806 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Feature Understanding and Sparsity Enhancement via 2-Layered kernel machines (2L-FUSE) Camattari, Fabiana Guastavino, Sabrina Marchetti, Francesco Perracchione, Emma Numerical Analysis Solar and Stellar Astrophysics Machine Learning 65D15, 41A05, 68Q32 We propose a novel sparsity enhancement strategy for regression tasks, based on learning a data-adaptive kernel metric, i.e., a shape matrix, through 2-Layered kernel machines. The resulting shape matrix, which defines a Mahalanobis-type deformation of the input space, is then factorized via an eigen-decomposition, allowing us to identify the most informative directions in the space of features. This data-driven approach provides a flexible, interpretable and accurate feature reduction scheme. Numerical experiments on synthetic and applications to real datasets of geomagnetic storms demonstrate that our approach achieves minimal yet highly informative feature sets without losing predictive performance. |
| title | Feature Understanding and Sparsity Enhancement via 2-Layered kernel machines (2L-FUSE) |
| topic | Numerical Analysis Solar and Stellar Astrophysics Machine Learning 65D15, 41A05, 68Q32 |
| url | https://arxiv.org/abs/2509.07806 |