通过主动学习优化流形高斯过程回归，提升高维空间预测精度。
Active Learning for Manifold Gaussian Process Regression
- 联合优化神经网络降维与潜在空间高斯过程回归
- 主动学习准则显著降低全局预测误差，优于随机采样
- 适合处理复杂不连续函数的科学工程建模场景
本文提出一种面向流形高斯过程回归的主动学习框架，将流形学习与策略性数据选择结合，以提升高维空间中的预测精度。方法联合优化用于降维的神经网络与潜在空间中的高斯过程回归器，由最小化全局预测误差的主动学习准则监督。在合成数据上的实验表明，该方法性能优于随机序列学习。框架能有效处理复杂、不连续函数，同时保持计算可 tractability（可计算性），为科学与工程应用提供实用价值。未来工作将聚焦于可扩展性及不确定性感知的流形学习。
原文摘要 · Abstract (English)
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.
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