3 citations · 3 across the 3 of their papers we have counts for
3 papers
math.NA2023★ 3 cited
Low-rank Tensor Train Decomposition Using TensorSketch
Zhongming Chen, Huilin Jiang, Gaohang Yu +1
Tensor train decomposition is one of the most powerful approaches for processing high-dimensional data. For low-rank tensor train decomposition of large tensors, the alternating le…
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…