文献类型：期刊文献

英文题名：CLUSTERING BASED MULTIPLE ANCHORS HIGH-DIMENSIONAL MODEL REPRESENTATION

作者：Xiong, Meixin Chen, Liuhong Ming, Ju Pan, Xingchen Yan, Xinyu

第一作者：Xiong, Meixin

机构：[1] School of Computer Science and Mathematics, Fujian University of Technology, Fujian, Fuzhou, 350118, China; [2] School of Mathematics and Statistics, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China; [3] Science College, Guizhou Institute of Technology, Guizhou, Guiyang, 550000, China

第一机构：School of Computer Science and Mathematics, Fujian University of Technology, Fujian, Fuzhou, 350118, China

年份：2023

外文期刊名：arXiv

收录：EI(收录号：20230389208)

语种：英文

外文关键词：Anchors - Computational geometry - Interpolation - Stochastic systems - Variable speed transmissions

摘要：In this work, a cut high-dimensional model representation (cut-HDMR) expansion based on multiple anchors is constructed via the clustering method. Specifically, a set of random input realizations is drawn from the parameter space and grouped by the centroidal Voronoi tessellation (CVT) method. Then for each cluster, the centroid is set as the reference, thereby the corresponding zeroth-order term can be determined directly. While for non-zero order terms of each cut-HDMR, a set of discrete points is selected for each input component, and the Lagrange interpolation method is applied. For a new input, the cut-HDMR corresponding to the nearest centroid is used to compute its response. Numerical experiments with high-dimensional integral and elliptic stochastic partial differential equation as backgrounds show that the CVT based multiple anchors cut-HDMR can alleviate the negative impact of a single inappropriate anchor point, and has higher accuracy than the average of several expansions. Copyright ? 2023, The Authors. All rights reserved.