%0 DATA
%A Sergei, Manzhos
%A Koichi, Yamashita
%A Tucker, Carrington Jr.
%D 2019
%T Fitting sparse multidimensional data with low-dimensional terms
%U https://mendeley.figshare.com/articles/Fitting_sparse_multidimensional_data_with_low-dimensional_terms/11333621
%R 10.17632/s89w4yyzbn.1
%2 https://mendeley.figshare.com/ndownloader/files/20094551
%K Computational Physics
%K Computational Method
%X Abstract
An algorithm that fits a continuous function to sparse multidimensional data is presented. The algorithm uses a representation in terms of lower-dimensional component functions of coordinates defined in an automated way and also permits dimensionality reduction. Neural networks are used to construct the component functions.
Title of program: RS_HDMR_NN
Catalogue Id: AEEI_v1_0
Nature of problem
Fitting a smooth, easily integratable and differentiatable, function to a very sparse (~2-3 points per dimension) multidimensional (D >= 6) large (~10 4 -10 5 data) dataset.
Versions of this program held in the CPC repository in Mendeley Data
AEEI_v1_0; RS_HDMR_NN; 10.1016/j.cpc.2009.05.022
This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)