论文标题
当地平滑的高斯过程回归
Locally Smoothed Gaussian Process Regression
论文作者
论文摘要
我们开发了一个新颖的框架来加速高斯流程回归（GPR）。特别是，我们考虑在每个数据点的本地化内核，以减少其他数据点的贡献，并且我们得出了源于这种本地化操作的GPR模型。通过一组实验，我们证明了与完整的GPR，其他本地化模型和深层过程相比，提出的方法的竞争性能。至关重要的是，与标准的全局GPR相比，由于定位操作引起的革兰氏矩阵的稀疏效果，这些性能是通过相当大的加速获得的。
We develop a novel framework to accelerate Gaussian process regression (GPR). In particular, we consider localization kernels at each data point to down-weigh the contributions from other data points that are far away, and we derive the GPR model stemming from the application of such localization operation. Through a set of experiments, we demonstrate the competitive performance of the proposed approach compared to full GPR, other localized models, and deep Gaussian processes. Crucially, these performances are obtained with considerable speedups compared to standard global GPR due to the sparsification effect of the Gram matrix induced by the localization operation.
