论文标题
高斯过程的混合类别相关内核
A mixed-categorical correlation kernel for Gaussian process
论文作者
论文摘要
最近，基于高斯工艺（GP）替代物的混合类别元模型的兴趣越来越大。在这种情况下，几种现有方法使用连续内核（例如连续放松和基于Gower距离的GP）或使用相关矩阵的直接估计来使用不同的策略。在本文中，我们提出了一种基于内核的方法，该方法扩展了连续的指数核以处理混合类别变量。提出的内核导致了一种新的GP替代物，该代元概括了连续的放松和基于Gower距离的GP模型。我们在分析和工程问题上都证明，我们提出的GP模型比其他基于内核的最先进模型更有可能更有可能和剩余误差。我们的方法可在开源软件SMT中获得。
Recently, there has been a growing interest for mixed-categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies either by using continuous kernels (e.g., continuous relaxation and Gower distance based GP) or by using a direct estimation of the correlation matrix. In this paper, we present a kernel-based approach that extends continuous exponential kernels to handle mixed-categorical variables. The proposed kernel leads to a new GP surrogate that generalizes both the continuous relaxation and the Gower distance based GP models. We demonstrate, on both analytical and engineering problems, that our proposed GP model gives a higher likelihood and a smaller residual error than the other kernel-based state-of-the-art models. Our method is available in the open-source software SMT.
