A Tight Lower Bound for the Approximation Guarantee of Higher-Order Singular Value Decomposition
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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.
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References · 6
- 2016Tensor Decomposition for Signal Processing and Machine Learningcited by 224
- 2017Tensor SVD: Statistical and Computational Limitscited by 53
- 2017Introduction to Tensor Decompositions and their Applications in Machine Learningcited by 46
- 2020Optimization Landscape of Tucker Decompositioncited by 10
- 2023Approximately Optimal Core Shapes for Tensor Decompositionscited by 8
- 2022Subquadratic Kronecker Regression with Applications to Tensor Decompositioncited by 6
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