A Bayesian Framework for Built-in Input Dimension Reduction for Gaussian Process Modeling
Gaussian process (GP) modeling is widely used in computational science and engineering. However, fitting a GP to high-dimensional inputs remains challenging due to the curse of dimensionality. While various methods have been proposed to reduce input dimensionality, they typically follow a two-stage approach, performing dimension reduction and GP fitting separately. We introduce a Bayesian framework that seamlessly integrates dimensionality reduction with GP modeling and inference. Our approach, built on a hierarchical Bayesian model with priors on the Stiefel manifold, enforces orthonormality on the projection matrix and enables posterior inference via Hamiltonian Monte Carlo with geodesi...
| Authors | Eric Herrison Gyamfi, Emily L. Kang, Bledar A. Konomi et al. |
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| Primary category | stat.ML |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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