Scholarlink

The study of randomized iterative methods for matrix-vector and matrix-matrix regression encompasses a rich body of literature. However, the study of such methods for tensor regression is still in its infancy. Our new work spans three different areas within the iterative method literature: methods for systems with factorized measurement operators, column-action methods, and methods for systems with adversarially corrupted measurements. In particular, we extend variants of the Kaczmarz and Gauss-Seidel methods to tensor regression under the t-product, which also yields novel insights into the standard matrix-vector and matrix-matrix regression settings. In addition, we survey the related work in the matrix-vector and tensor regression literature. We provide a suite of numerical experiments that illustrate the strengths and weaknesses of our proposed methods and demonstrate their application to image deblurring.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Randomized Iterative Methods for Tensor Regression Under the t-Product

  • 章节

  • 发表日期：

    2025年

查看全文
  • Alejandra Castillo,
  • Jamie Haddock,
  • Iryna Hartsock,
  • Paulina Hoyos,
  • Lara Kassab,
  • Alona Kryshchenko,
  • Kamila Larripa,
  • Deanna Needell,
  • Shambhavi Suryanarayanan,
  • Karamatou Yacoubou Djima

摘要

The study of randomized iterative methods for matrix-vector and matrix-matrix regression encompasses a rich body of literature. However, the study of such methods for tensor regression is still in its infancy. Our new work spans three different areas within the iterative method literature: methods for systems with factorized measurement operators, column-action methods, and methods for systems with adversarially corrupted measurements. In particular, we extend variants of the Kaczmarz and Gauss-Seidel methods to tensor regression under the t-product, which also yields novel insights into the standard matrix-vector and matrix-matrix regression settings. In addition, we survey the related work in the matrix-vector and tensor regression literature. We provide a suite of numerical experiments that illustrate the strengths and weaknesses of our proposed methods and demonstrate their application to image deblurring.