A sequential multilinear Nyström algorithm for streaming low-rank approximation of tensors in Tucker format

DOI10.1016/j.aml.2024.109271zbMATH Open1548.65101MaRDI QIDQ6620442

Behnam Hashemi, Alberto Bucci

Publication date: 16 October 2024

Published in: Applied Mathematics Letters (Search for Journal in Brave)

zbMATH Keywords

Nyström methodlow-rank approximationtensorsTucker decompositionrandomized linear algebra

Mathematics Subject Classification ID

Multilinear algebra, tensor calculus (15A69) Randomized algorithms (68W20) Numerical methods for low-rank matrix approximation; matrix compression (65F55)

Cites Work

• Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
• An efficient randomized algorithm for computing the approximate Tucker decomposition
• Randomized algorithms for the approximations of Tucker and the tensor train decompositions
• A New Truncation Strategy for the Higher-Order Singular Value Decomposition
• Algorithm 862
• Practical Sketching Algorithms for Low-Rank Matrix Approximation
• Randomized Algorithms for Low-Rank Tensor Decompositions in the Tucker Format
• Faster Johnson–Lindenstrauss transforms via Kronecker products
• Low-Rank Tucker Approximation of a Tensor from Streaming Data
• Numerical linear algebra in the streaming model
• Approximation strategy based on the T-product for third-order quaternion tensors with application to color video compression
• Fast randomized numerical rank estimation for numerically low-rank matrices

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