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We begin this journey by reviewing some techniques able to extract the intrinsic dimensionality of a data set. These techniques have been employed successfully in different engineering applications and have revealed to be of an utmost importance in the treatment of big data sets [1–3].

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Intrinsic Dimensionality of a Data Set and Manifold Learning

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Francisco Chinesta,
  • Elías Cueto,
  • Victor Champaney,
  • Chady Ghnatios,
  • Amine Ammar,
  • Nicolas Hascoët,
  • David González,
  • Icíar Alfaro,
  • Daniele Di Lorenzo,
  • Angelo Pasquale,
  • Dominique Baillargeat

摘要

We begin this journey by reviewing some techniques able to extract the intrinsic dimensionality of a data set. These techniques have been employed successfully in different engineering applications and have revealed to be of an utmost importance in the treatment of big data sets [1–3].