Paper Info
Title
Factorized form of the indexing HDMR method for multivariate data modeling
Abstract
A multivariate data set including a number of scattered nodes with associated function values is given in data modeling problems to construct a rule for the estimation process of unknown function values. To reduce the mathematical and computational complexity of the given problem coming from the multivariance, the given multivariate data set may be partitioned into less-variate data sets with one or two variables. The indexing HDMR method, which is a very recently developed divide-and-conquer method, can be used for this data partitioning process. However, we know that this method works well for the problems with either a dominantly or purely additive nature. To improve the overall performance of the method for different cases, this work offers the factorized form of the Indexing HDMR method. The Factorized Indexing HDMR method uses the Indexing HDMR components to construct the analytical models for the given multivariate problems and impressive numerical results are obtained.
Year
DOI
Venue
2013
10.1016/j.mcm.2012.10.034
Mathematical and Computer Modelling
Keywords
Field
DocType
interpolation,multivariate data modeling,high dimensional model representation,approximation,multidimensional problems
Data modeling,Data mining,Mathematical optimization,Data set,Multivariate statistics,Interpolation,Search engine indexing,High-dimensional model representation,Associated function,Mathematics,Computational complexity theory
Journal
Volume
Issue
ISSN
57
5-6
0895-7177
Citations 
PageRank 
References 
0
0.34
10
Authors
1
Name
Order
Citations
PageRank
M. Alper Tunga1405.44