Gaussian Processes for High-Dimensional, Large Data Sets: A Review
Gaussian Processes for High-Dimensional, Large Data Sets: A Review
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DOI：
10.1109/wsc57314.2022.10015416
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Mengrui Jiang;Giulia Pedrielli;S. Ng
中科院分区：
文献类型：
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作者：
Mengrui Jiang;Giulia Pedrielli;S. Ng
众所周知，高斯过程在工程、科学、经济等多个领域有着广泛的用途，它在控制模型复杂性的同时，显示了几种替代方法的重要优势。然而，由于似然函数的难解性和方差协方差矩阵的增长，对于高维以及大样本大小的输入，这一族模型的使用受到阻碍。本文通过对这些挑战进行分类，研究了这些挑战的最新解决方案。我们的目标是选择覆盖每个类别的几种算法，并进行实证实验，以比较它们在同一组测试函数上的性能。我们的初步结果集中在一组选定方法的确定性实现上。实验结果可能会对未来想要研究和使用高斯过程解决高维和大数据集问题的读者起到指导作用。
Gaussian processes, known to have versatile uses in several fields across engineering, science, economics, show important advantages to several alternative approaches while controlling model complexity. However, the use of this family of models is hindered for inputs that are high dimensional as well as large sample sizes due to the intractability of the likelihood function, and the growth of the variance covariance matrix. This article investigates state-of-art solutions to these challenges according classifying them into categories. The goal is to select several algorithms covering each category and perform empirical experiments to compare their performances on the same set of test functions. Our preliminary results focus on deterministic implementations of a set of selected approaches. The results of the experiments may serve as a guidance to future readers who want to study and use Gaussian process in problems with high dimensions and big data sets.