论文标题
基于迭代修剪的强大高斯过程回归
Robust Gaussian Process Regression Based on Iterative Trimming
论文作者
论文摘要
当数据被异常值污染时，高斯过程（GP）回归可能会严重偏差。本文提出了一种新的强大的GP回归算法，该算法迭代地修剪了最极端的数据点。尽管新算法将标准GP作为非参数和灵活的回归方法保留了吸引人的特性，但即使在存在极端或丰富的异常值的情况下，它也可以极大地提高受污染数据的模型准确性。与以前依赖近似推断的强大GP变体相比，它也更容易实现。该方法应用于具有不同污染水平的广泛实验，在大多数测试案例中，拟议的方法显着超过了标准GP和流行的鲁棒GP变体。此外，作为天体物理研究中的一个实际示例，我们表明该方法可以精确地确定星形簇的颜色刻度图中的主要序列脊线。
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.
