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Very often, manifold learning techniques employ some form of local least-squares fitting of the data. The novelty in compressed sensing techniques relies in the use of the L1-norm instead [1]. The L1 norm constitutes an elegant way of enforcing sparsity. In regression, L2 norm gives an utmost importance to the outliers. This is due to the use of the squared norm, such that these outliers have a tremendous influence in the resulting fitted curve.

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Compressed Sensing

  • 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

Very often, manifold learning techniques employ some form of local least-squares fitting of the data. The novelty in compressed sensing techniques relies in the use of the L1-norm instead [1]. The L1 norm constitutes an elegant way of enforcing sparsity. In regression, L2 norm gives an utmost importance to the outliers. This is due to the use of the squared norm, such that these outliers have a tremendous influence in the resulting fitted curve.