High-Dimensional Intrinsic Interpolation Using Gaussian Process Regression and Diffusion Maps

Charanraj A. Thimmisetty; Roger G. Ghanem; Joshua A. White; Xiao Chen

doi:10.1007/s11004-017-9705-y

High-Dimensional Intrinsic Interpolation Using Gaussian Process Regression and Diffusion Maps

Mathematical Geosciences ◽

10.1007/s11004-017-9705-y ◽

2017 ◽

Vol 50 (1) ◽

pp. 77-96 ◽

Cited By ~ 3

Author(s):

Charanraj A. Thimmisetty ◽

Roger G. Ghanem ◽

Joshua A. White ◽

Xiao Chen

Keyword(s):

Gaussian Process ◽

Gaussian Process Regression ◽

High Dimensional ◽

Diffusion Maps ◽

And Diffusion

Download Full-text

Clustering of High-Dimensional Gene Expression Data with Feature Filtering Methods and Diffusion Maps

2008 International Conference on BioMedical Engineering and Informatics ◽

10.1109/bmei.2008.256 ◽

2008 ◽

Cited By ~ 3

Author(s):

Rui Xu ◽

Steven Damelin ◽

Boaz Nadler ◽

Donald C. Wunsch II

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

High Dimensional ◽

Expression Data ◽

Diffusion Maps ◽

Feature Filtering ◽

And Diffusion

Download Full-text

Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for representing multidimensional functions with machine-learned lower-dimensional terms allowing insight with a general method

Computer Physics Communications ◽

10.1016/j.cpc.2021.108220 ◽

2021 ◽

pp. 108220

Author(s):

Owen Ren ◽

Mohamed Ali Boussaidi ◽

Dmitry Voytsekhovsky ◽

Manabu Ihara ◽

Sergei Manzhos

Keyword(s):

Gaussian Process ◽

Random Sampling ◽

Gaussian Process Regression ◽

Model Representation ◽

High Dimensional ◽

High Dimensional Model Representation ◽

Dimensional Model ◽

Lower Dimensional ◽

General Method

Download Full-text

Revisiting the Gaussian process regression for fitting high-dimensional potential energy surface and its application to the OH + HO2 → O2 + H2O reaction

The Journal of Chemical Physics ◽

10.1063/1.5143544 ◽

2020 ◽

Vol 152 (13) ◽

pp. 134309 ◽

Cited By ~ 3

Author(s):

Qingfei Song ◽

Qiuyu Zhang ◽

Qingyong Meng

Keyword(s):

Potential Energy ◽

Potential Energy Surface ◽

Gaussian Process ◽

Energy Surface ◽

Gaussian Process Regression ◽

High Dimensional

Download Full-text

Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold

Computer Methods in Applied Mechanics and Engineering ◽

10.1016/j.cma.2020.113269 ◽

2020 ◽

Vol 370 ◽

pp. 113269

Author(s):

D.G. Giovanis ◽

M.D. Shields

Keyword(s):

Gaussian Process ◽

Grassmann Manifold ◽

Gaussian Process Regression ◽

Data Driven ◽

High Dimensional ◽

Dimensional Models

Download Full-text

Kernel principal component analysis-based Gaussian process regression modelling for high-dimensional reliability analysis

Computers & Structures ◽

10.1016/j.compstruc.2020.106358 ◽

2020 ◽

Vol 241 ◽

pp. 106358 ◽

Cited By ~ 2

Author(s):

Tong Zhou ◽

Yongbo Peng

Keyword(s):

Principal Component Analysis ◽

Gaussian Process ◽

Reliability Analysis ◽

Gaussian Process Regression ◽

Principal Component ◽

Component Analysis ◽

Kernel Principal Component Analysis ◽

High Dimensional ◽

Regression Modelling

Download Full-text

Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear Data

Biometrics ◽

10.1111/j.1541-0420.2011.01576.x ◽

2011 ◽

Vol 67 (4) ◽

pp. 1285-1294 ◽

Cited By ~ 16

Author(s):

G. Yi ◽

J. Q. Shi ◽

T. Choi

Keyword(s):

Gaussian Process ◽

Gaussian Process Regression ◽

High Dimensional

Download Full-text

Clustering of high-dimensional gene expression data with feature filtering methods and diffusion maps

Artificial Intelligence in Medicine ◽

10.1016/j.artmed.2009.06.001 ◽

2010 ◽

Vol 48 (2-3) ◽

pp. 91-98 ◽

Cited By ~ 20

Author(s):

Rui Xu ◽

Steven Damelin ◽

Boaz Nadler ◽

Donald C. Wunsch

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

High Dimensional ◽

Expression Data ◽

Diffusion Maps ◽

Feature Filtering ◽

And Diffusion

Download Full-text

Assessing Gaussian Process Regression and Permutationally Invariant Polynomial Approaches To Represent High-Dimensional Potential Energy Surfaces

Journal of Chemical Theory and Computation ◽

10.1021/acs.jctc.8b00298 ◽

2018 ◽

Vol 14 (7) ◽

pp. 3381-3396 ◽

Cited By ~ 39

Author(s):

Chen Qu ◽

Qi Yu ◽

Brian L. Van Hoozen ◽

Joel M. Bowman ◽

Rodrigo A. Vargas-Hernández

Keyword(s):

Potential Energy ◽

Gaussian Process ◽

Potential Energy Surfaces ◽

Gaussian Process Regression ◽

High Dimensional ◽

Invariant Polynomial ◽

Energy Surfaces

Download Full-text

Variable selection using Gaussian process regression-based metrics for high-dimensional model approximation with limited data

Structural and Multidisciplinary Optimization ◽

10.1007/s00158-018-2137-6 ◽

2018 ◽

Vol 59 (5) ◽

pp. 1439-1454 ◽

Cited By ~ 6

Author(s):

Kyungeun Lee ◽

Hyunkyoo Cho ◽

Ikjin Lee

Keyword(s):

Variable Selection ◽

Gaussian Process ◽

Gaussian Process Regression ◽

High Dimensional ◽

Limited Data ◽

Dimensional Model ◽

Model Approximation

Download Full-text

Gaussian Process Regression Method for Classification for High-Dimensional Data with Limited Samples

2018 Eighth International Conference on Information Science and Technology (ICIST) ◽

10.1109/icist.2018.8426077 ◽

2018 ◽

Cited By ~ 8

Author(s):

Nian Zhang ◽

Jiang Xiong ◽

Jing Zhong ◽

Keenan Leatham

Keyword(s):

Gaussian Process ◽

High Dimensional Data ◽

Gaussian Process Regression ◽

Regression Method ◽

High Dimensional

Download Full-text