26 citations · 137 across the 17 of their papers we have counts for
6 papers · 1 filter
Faster Tensor Train Decomposition for Sparse Data
Lingjie Li, Wenjian Yu, Kim Batselier
In recent years, the application of tensors has become more widespread in fields that involve data analytics and numerical computation. Due to the explosive growth of data, low-ran…
Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains
Ching-Yun Ko, Kim Batselier, Wenjian Yu +1
We propose a new tensor completion method based on tensor trains. The to-be-completed tensor is modeled as a low-rank tensor train, where we use the known tensor entries and their…
Computing low-rank approximations of large-scale matrices with the Tensor Network randomized SVD
Kim Batselier, Wenjian Yu, Luca Daniel +1
We propose a new algorithm for the computation of a singular value decomposition (SVD) low-rank approximation of a matrix in the Matrix Product Operator (MPO) format, also called t…
A Fast Implementation of Singular Value Thresholding Algorithm using Recycling Rank Revealing Randomized Singular Value Decomposition
Yaohang Li, Wenjian Yu
In this paper, we present a fast implementation of the Singular Value Thresholding (SVT) algorithm for matrix completion. A rank-revealing randomized singular value decomposition (…
Efficient Randomized Algorithms for the Fixed-Precision Low-Rank Matrix Approximation
Wenjian Yu, Yu Gu, Yaohang Li
Randomized algorithms for low-rank matrix approximation are investigated, with the emphasis on the fixed-precision problem and computational efficiency for handling large matrices.…
A Rank Revealing Randomized Singular Value Decomposition (R3SVD) Algorithm for Low-rank Matrix Approximations
Hao Ji, Wenjian Yu, Yaohang Li
In this paper, we present a Rank Revealing Randomized Singular Value Decomposition (R3SVD) algorithm to incrementally construct a low-rank approximation of a potentially large matr…