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math.NA2023
A Randomized Block Krylov Method for Tensor Train Approximation
Gaohang Yu, Jinhong Feng, Zhongming Chen +2
Tensor train decomposition is a powerful tool for dealing with high-dimensional, large-scale tensor data, which is not suffering from the curse of dimensionality. To accelerate the…
math.OC2023
Variable T-Product and Zero-Padding Tensor Completion with Applications
Liqun Qi, Rui Yan, Ziyan Luo +2
The T-product method based upon Discrete Fourier Transformation (DFT) has found wide applications in engineering, in particular, in image processing. In this paper, we propose vari…
math.NA2023
Practical Sketching Algorithms for Low-Rank Tucker Approximation of Large Tensors
Wandi Dong, Gaohang Yu, Liqun Qi +1
Low-rank approximation of tensors has been widely used in high-dimensional data analysis. It usually involves singular value decomposition (SVD) of large-scale matrices with high c…