提出一种能自适应抗异常值的生成式高斯过程回归方法。
Variational Outlier-Robust Gaussian Process Regression with Generative Modeling
- 通过生成模型建模观测污染，动态识别并削弱异常值影响。
- 在合成与真实数据上，预测精度优于或媲美现有鲁棒方法。
- 计算复杂度保持立方级，适合中等规模数据集应用。
异常值会因传统高斯似然函数导致高斯过程回归（GPR）严重失真，影响模型学习与预测准确性。本文提出一种生成式GPR模型，能够捕捉特定观测的污染情况，并自适应地减轻异常值的影响。采用变分广义期望-最大化算法来学习潜在变量和GPR模型参数。在不同污染设置下的合成与真实数据集实验表明，所提方法在预测精度上保持竞争力，且在多个场景下优于现有鲁棒GPR基线方法。此外，该方法的计算复杂度仍为立方级，与对比的GPR方法一致。
原文摘要 · Abstract (English)
Outliers can substantially distort Gaussian process regression (GPR) due to its conventional Gaussian observation likelihood, leading to inaccurate model learning and prediction. To address this limitation, this article introduces a generative GPR model that captures observation-specific contamination and adaptively mitigates the influence of outliers. Subsequently, a variational generalized expectation-maximization procedure is used to learn the latent variables and GPR model parameters. Experiments on synthetic and real datasets under different contamination settings demonstrate that the proposed method remains competitive with-and in several cases outperforms-robust GPR baselines in prediction accuracy. Moreover, the proposed method shares the cubic computational scaling of the compared GPR methods.
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