论文标题
稀疏的高斯过程中的结节选择
Knot Selection in Sparse Gaussian Processes
论文作者
论文摘要
基于结的基于结的稀疏高斯工艺取得了相当大的成功，这是对完整高斯工艺的可扩展近似值。但是，当选择结时，可能会出现问题。例如，边缘可能性表面是高度多模式的，这可能会导致次优的放置，其中一些结几乎没有功能。当使用多个打结的情况下，这尤其是一个问题，因此准确性几乎没有获得的额外计算成本。 我们提出了一种一次性的结选择算法，以选择结的数量和位置。我们的算法使用贝叶斯优化来有效提出可能是好的结，并且在很大程度上避免了使用边缘可能性作为目标函数时遇到的病理。我们提供的经验结果表明，在当前标准方法上的准确性和速度提高了。
Knot-based, sparse Gaussian processes have enjoyed considerable success as scalable approximations to full Gaussian processes. Problems can occur, however, when knot selection is done by optimizing the marginal likelihood. For example, the marginal likelihood surface is highly multimodal, which can cause suboptimal knot placement where some knots serve practically no function. This is especially a problem when many more knots are used than are necessary, resulting in extra computational cost for little to no gains in accuracy. We propose a one-at-a-time knot selection algorithm to select both the number and placement of knots. Our algorithm uses Bayesian optimization to efficiently propose knots that are likely to be good and largely avoids the pathologies encountered when using the marginal likelihood as the objective function. We provide empirical results showing improved accuracy and speed over the current standard approaches.
