主 题: Dimension Reduction for Gaussian Process Models via Convex Combination of Kernels
内容简介: Some engineering and scientific computer models that have high dimensional input space are actually only affected by a few essential input variables. It poses great computation challenges to use the classic Gaussian Process models to analyze such experimental data with high dimensional input. If these active variables are identified, it would reduce the computation in the estimation of the Gaussian process (GP) model and help researchers understand the system modeled by the computer simulation. More importantly, reducing the input dimensions would also increase the prediction accuracy, as it alleviates the ``curse of dimensionality problem. In this talk, we propose a new approach to reduce the input dimension of the Gaussian process model. Specifically, we develop an optimization method to identify a convex combination of a subset of kernels of lower dimensions from a large candidate set of kernels, as the correlation function for the GP model. To make sure a sparse subset is selected, we incorporate the effect heredity principle in the forward and backward selection of the low-dimensional kernel functions. The proposed method has many connections with the existing methods including active subspace, additive GP, and composite GP models in the Uncertainty Quantification literature.
报告人: Lulu Kang 副教授、博士
时 间: 2018-06-19 14:30
地 点: 竞慧东楼302
举办单位: 统计与数学学院 沁园书院
