Robust Gaussian process regression based on iterative trimming
Robust Gaussian process regression based on iterative trimming
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DOI：
10.1016/j.ascom.2021.100483
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发表时间：
2021-06-18
影响因子：
2.5
通讯作者：
Shao, Zhengyi
中科院分区：
文献类型：
--
作者：
Li, Zhao-Zhou;Li, Lu;Shao, Zhengyi
高斯过程（GP）回归可以严重偏差时，数据被污染的离群值。本文提出了一种新的鲁棒GP回归算法，迭代修剪最极端的数据点。虽然新算法保留了标准GP作为非参数和灵活的回归方法的吸引人的特性，但即使在存在极端或大量离群值的情况下，它也可以大大提高污染数据的模型精度。与依赖于近似推理的先前鲁棒GP变体相比，它也更容易实现。应用到广泛的实验与不同的污染水平，所提出的方法显着优于标准GP和流行的鲁棒GP变体与学生t似然在大多数测试情况下。此外，作为天体物理研究中的一个实例，我们表明该方法可以精确地确定星星团色星等图中的主序星脊线。(C)2021爱思唯尔有限公司版权所有。
The Gaussian process (GP) regression can be severely biased when the data are contaminated by outliers. This paper presents a new robust GP regression algorithm that iteratively trims the most extreme data points. While the new algorithm retains the attractive properties of the standard GP as a nonparametric and flexible regression method, it can greatly improve the model accuracy for contaminated data even in the presence of extreme or abundant outliers. It is also easier to implement compared with previous robust GP variants that rely on approximate inference. Applied to a wide range of experiments with different contamination levels, the proposed method significantly outperforms the standard GP and the popular robust GP variant with the Student-t likelihood in most test cases. In addition, as a practical example in the astrophysical study, we show that this method can precisely determine the main-sequence ridge line in the color-magnitude diagram of star clusters. (C) 2021 Elsevier B.V. All rights reserved.