Fitting sparse multidimensional data with low-dimensional terms

Published: 1 October 2009| Version 1 | DOI: 10.17632/s89w4yyzbn.1
Sergei Manzhos, Koichi Yamashita, Tucker Carrington Jr.


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)



Computational Physics, Computational Method