mlsanityData to 14 Oct 2026

A Tight Lower Bound for the Approximation Guarantee of Higher-Order Singular Value Decomposition

arXiv 2508.06693 ↗First posted 8 Aug 2025cs.DS cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

We prove that the classic approximation guarantee for the higher-order singular value decomposition (HOSVD) is tight by constructing a tensor for which HOSVD achieves an approximation ratio of N/(1+varepsilon), for any varepsilon > 0. This matches the upper bound of De Lathauwer et al. (2000a) and shows that the approximation ratio of HOSVD cannot be improved. Using a more advanced construction, we also prove that the approximation guarantees for the ST-HOSVD algorithm of Vannieuwenhoven et al. (2012) and higher-order orthogonal iteration (HOOI) of De Lathauwer et al. (2000b) are tight by showing that they can achieve their worst-case approximation ratio of N / (1 + varepsilon), for any varepsilon > 0.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 18 references that are in the map. First posted on arXiv 8 Aug 2025.

Builds on

6 of 18 references.

References · 6

  1. 2016Tensor Decomposition for Signal Processing and Machine Learningcited by 224
  2. 2017Tensor SVD: Statistical and Computational Limitscited by 53
  3. 2017Introduction to Tensor Decompositions and their Applications in Machine Learningcited by 46
  4. 2020Optimization Landscape of Tucker Decompositioncited by 10
  5. 2023Approximately Optimal Core Shapes for Tensor Decompositionscited by 8
  6. 2022Subquadratic Kronecker Regression with Applications to Tensor Decompositioncited by 6

Built on by

Not cited yet.