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Selecting among three-mode principal component models of different types and complexities: A numerical convex hull based method

机译:在不同类型和复杂性的三模式主成分模型中进行选择:一种基于数值凸包的方法

摘要

Several three-mode principal component models can be considered for the modelling of three-way, three-mode data, including the Candecomp/Parafac, Tucker3, Tucker2, and Tucker I models. The following question then may be raised: given a specific data set, which of these models should be selected, and at what complexity (i.e. with how many components)? We address this question by proposing a numerical model selection heuristic based on a convex hull. Simulation results show that this heuristic performs almost perfectly, except for Tucker3 data arrays with at least one small mode and a relatively large amount of error.
机译:可以考虑使用几种三模式主成分模型对三向,三模式数据进行建模,包括Candecomp / Parafac,Tucker3,Tucker2和Tucker I模型。然后可能会提出以下问题:给定一个特定的数据集,应该选择这些模型中的哪一个,以及具有什么复杂度(即具有多少个组件)?我们通过提出基于凸包的数值模型选择启发法来解决这个问题。仿真结果表明,该启发式算法性能非常理想,除了具有至少一种小模式和相对较大误差的Tucker3数据阵列之外。

著录项

  • 作者

    Ceulemans Eva; Kiers HAL;

  • 作者单位
  • 年度 2006
  • 总页数
  • 原文格式 PDF
  • 正文语种 en
  • 中图分类

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