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Graph based Gaussian processes on restricted domains
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DOI：10.1111/rssb.12486.png)
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摘要
En 中文
In nonparametric regression, it is common for the inputs to fall in a restricted subset of Euclidean space. Typical kernel-based methods that do not take into account the intrinsic geometry of the domain across which observations are collected may produce sub-optimal results. In this article, we focus on solving this problem in the context of Gaussian process (GP) models, proposing a new class of Graph Laplacian based GPs (GL-GPs), which learn a covariance that respects the geometry of the input domain. As the heat kernel is intractable computationally, we approximate the covariance using finitely-many eigenpairs of the Graph Laplacian (GL). The GL is constructed from a kernel which depends only on the Euclidean coordinates of the inputs. Hence, we can benefit from the full knowledge about the kernel to extend the covariance structure to newly arriving samples by a Nystrom type extension. We provide substantial theoretical support for the GL-GP methodology, and illustrate performance gains in various applications.
Keyword:
Bayesian
graph Laplacian
heat kernel
manifold
nonparametric regression
restricted domain
semi-supervised
期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.2W
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分享收藏ADAPTIVE BAYESIAN ESTIMATION USING A GAUSSIAN RANDOM FIELD WITH INVERSE GAMMA BANDWIDTH.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
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