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Geometric Linear Regression and Geometric Relation

  • Kaijun Wang
  • Liying Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

When a linear regression model is constructed by statistical calculation, all the data are treated without order, even if they are order data. We propose the Geometric regression and geometric relation method (GR2) to utilize the relation information inside the order of data. The GR2 transforms the order data of each variable to a curve (or geometric relation), and uses the curves to establish a geometric regression model. The prediction method using this geometric regression model is developed to give predictions. Experimental results on simulated and real datasets show that the GR2 method is effective and has lower prediction errors than traditional linear regression.

Keywords

relations between data geometric regression geometric relation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kaijun Wang
    • 1
  • Liying Yang
    • 2
  1. 1.School of Mathematics & ComputerFujian Normal UniversityFuzhouP.R. China
  2. 2.School of Computer Science and TechnologyXidian UniversityXianP.R. China

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