A novel method for multivariate data modelling: Piecewise Generalized EMPR

TUNGA M. A., Demiralp M.

JOURNAL OF MATHEMATICAL CHEMISTRY, vol.51, no.10, pp.2654-2667, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 51 Issue: 10
  • Publication Date: 2013
  • Doi Number: 10.1007/s10910-013-0228-6
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2654-2667
  • Istanbul Technical University Affiliated: Yes


A multivariate data modelling problem consists of a number of nodes with associated function values. Increase in multivariance urges us to use divide-and-conquer algorithms in modelling process of these problems. High dimensional model representation based methods can partition a given multivariate data set into less-variate data sets and have the ability of building a model through these partitioned data sets. Generalized HDMR (GHDMR) is one of these methods and it is known that it works well for dominantly and purely additive natures. Piecewise Generalized HDMR is an alternative method and was developed to increase the efficiency of GHDMR but the performance of the method for modelling multiplicative natures is still not sufficient and acceptable. This work aims to develop a new piecewise method based on enhanced multivariance product representation which works well for representing multiplicative natures.