论文标题
将当地高斯流程分开的实时回归
Real-Time Regression with Dividing Local Gaussian Processes
论文作者
论文摘要
对在线预测的需求不断增加，大型数据集的可用性不断增长，这推动了对计算高效模型的需求。虽然确切的高斯过程回归显示出各种有利的理论属性（不确定性估计，无限表达能力），但相对于训练集尺寸的缩放范围较差，禁止其在大数据方案中实时应用。因此，本文提出了划分局部高斯过程，这是一种基于高斯过程回归的新型计算高效建模方法。由于输入空间的迭代性，数据驱动的划分，它们在实践中的训练点总数中达到了均匀的计算复杂性，同时提供了出色的预测分布。对现实世界数据集的数值评估表明，在准确性以及预测和更新速度方面，它们比其他最先进方法的优势。
The increased demand for online prediction and the growing availability of large data sets drives the need for computationally efficient models. While exact Gaussian process regression shows various favorable theoretical properties (uncertainty estimate, unlimited expressive power), the poor scaling with respect to the training set size prohibits its application in big data regimes in real-time. Therefore, this paper proposes dividing local Gaussian processes, which are a novel, computationally efficient modeling approach based on Gaussian process regression. Due to an iterative, data-driven division of the input space, they achieve a sublinear computational complexity in the total number of training points in practice, while providing excellent predictive distributions. A numerical evaluation on real-world data sets shows their advantages over other state-of-the-art methods in terms of accuracy as well as prediction and update speed.
