Modeling interactive components by coordinate kernel polynomial models

Xin Guo, Lexin Li, Qiang Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

We proposed the use of coordinate kernel polynomials in kernel regression. This new approach, called coordinate kernel polynomial regression, can simultaneously identify active variables and effective interactive components. Reparametrization refinement is found critical to improve the modeling accuracy and prediction power. The post-training component selection allows one to identify effective interactive components. Generalization error bounds are used to explain the effectiveness of the algorithm from a learning theory perspective and simulation studies are used to show its empirical effectiveness.

Original languageEnglish
Pages (from-to)263-277
Number of pages15
JournalMathematical Foundations of Computing
Volume3
Issue number4
DOIs
Publication statusPublished - Nov 2020

Keywords

  • coordinate kernel polynomial model
  • generalization
  • information criterion
  • Interactive component
  • kernel method
  • reparametrization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Theoretical Computer Science

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