Functional Dimension Reduction in Predictive Modeling

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Abstract

Abstract: The paper considers the problem of dimension reduction in metamodeling (predictive modeling). It is shown that to construct an exact metamodel based on reduced data, it is necessary to solve the problem of dimension reduction in a nonstandard formulation with due account of the additional functional constraints. To construct a tangent affine linear subspace from the data, which is required for solving the functional dimension reduction problem, a generalized principal component construction problem is formulated and solved.

Original languageEnglish
Pages (from-to)745-753
Number of pages9
JournalJournal of Communications Technology and Electronics
Volume66
Issue number6
DOIs
Publication statusPublished - Jun 2021

Keywords

  • dimension reduction
  • functional dimension reduction
  • generalized problem of principal component analysis
  • manifold learning

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