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Active Learning for Manifold Gaussian Process Regression

Active Learning for Manifold Gaussian Process Regression

来源:Arxiv_logoArxiv
英文摘要

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.

Yuanxing Cheng、Lulu Kang、Yiwei Wang、Chun Liu

计算技术、计算机技术

Yuanxing Cheng,Lulu Kang,Yiwei Wang,Chun Liu.Active Learning for Manifold Gaussian Process Regression[EB/OL].(2025-06-26)[2025-07-24].https://arxiv.org/abs/2506.20928.点此复制

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