用低维投影提升高斯过程训练效率，兼顾精度与速度。
Efficient Gaussian process learning via subspace projections
- 通过数据低维线性投影构建新型似然函数
- 在中等规模数据上优于精确GP和稀疏变分方法
- 随机投影可有效减少信息损失，适合大规模学习
我们提出一种基于数据低维线性投影的新型高斯过程训练目标，称为投影似然（Projected Likelihood, PL）。我们推导了PL相关的信息损失闭式表达，并实证表明在单位球面上进行随机投影可有效降低该损失。实验显示，PL在不同优化器、核函数和中等规模数据集上，相比精确高斯过程训练和稀疏高斯过程的变分自由能方法，在精度和计算效率方面均表现更优。
原文摘要 · Abstract (English)
We propose a novel training objective for GPs constructed using lower-dimensional linear projections of the data, referred to as \emph{projected likelihood} (PL). We provide a closed-form expression for the information loss related to the PL and empirically show that it can be reduced with random projections on the unit sphere. We show the superiority of the PL, in terms of accuracy and computational efficiency, over the exact GP training and the variational free energy approach to sparse GPs over different optimisers, kernels and datasets of moderately large sizes.
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