Active Learning for Manifold Gaussian Process Regression
This work addresses the challenge of efficient regression in high-dimensional scientific and engineering applications, though it appears incremental as it builds on existing manifold and Gaussian process methods.
The paper tackles the problem of improving regression accuracy in high-dimensional spaces by introducing an active learning framework that combines manifold learning with strategic data selection, demonstrating superior performance over random sequential learning on synthetic data.
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.