Active Learning for Manifold Gaussian Process Regression
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916000488751104 |
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| author | Cheng, Yuanxing Kang, Lulu Wang, Yiwei Liu, Chun |
| author_facet | Cheng, Yuanxing Kang, Lulu Wang, Yiwei Liu, Chun |
| contents | This paper introduces an active learning framework for manifold Gaussian Process (GP) regression, combining manifold learning with strategic data selection to improve accuracy in high-dimensional spaces. Our method jointly optimizes a neural network for dimensionality reduction and a Gaussian process regressor in the latent space, supervised by an active learning criterion that minimizes global prediction error. Experiments on synthetic data demonstrate superior performance over randomly sequential learning. The framework efficiently handles complex, discontinuous functions while preserving computational tractability, offering practical value for scientific and engineering applications. Future work will focus on scalability and uncertainty-aware manifold learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20928 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Active Learning for Manifold Gaussian Process Regression Cheng, Yuanxing Kang, Lulu Wang, Yiwei Liu, Chun Machine Learning 62 G.3 This paper introduces an active learning framework for manifold Gaussian Process (GP) regression, combining manifold learning with strategic data selection to improve accuracy in high-dimensional spaces. Our method jointly optimizes a neural network for dimensionality reduction and a Gaussian process regressor in the latent space, supervised by an active learning criterion that minimizes global prediction error. Experiments on synthetic data demonstrate superior performance over randomly sequential learning. The framework efficiently handles complex, discontinuous functions while preserving computational tractability, offering practical value for scientific and engineering applications. Future work will focus on scalability and uncertainty-aware manifold learning. |
| title | Active Learning for Manifold Gaussian Process Regression |
| topic | Machine Learning 62 G.3 |
| url | https://arxiv.org/abs/2506.20928 |