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Tensor decompositions allow us to express a high-order tensor as a product of lower dimensional tensors. Singular Value Decomposition, SVD, is very well know for factorizing matrices. The extension of SVD to high-dimensional settings, which is referred to as high order singular value decomposition, HOSVD, proceeds with higher-dimensional tensors.

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Tensor Formats and Tensor Decompositions

  • 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

Tensor decompositions allow us to express a high-order tensor as a product of lower dimensional tensors. Singular Value Decomposition, SVD, is very well know for factorizing matrices. The extension of SVD to high-dimensional settings, which is referred to as high order singular value decomposition, HOSVD, proceeds with higher-dimensional tensors.