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.

中文翻译：

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预测建模中的功能降维

摘要

论文考虑了元建模（预测建模）中的降维问题。结果表明，要基于缩减数据构建精确的元模型，必须在适当考虑附加功能约束的情况下解决非标准公式中的降维问题。为了从数据构建切线仿射线性子空间，这是解决函数降维问题所必需的，我们制定并解决了广义主成分构建问题。