1 citations · 1 across the 3 of their papers we have counts for
5 papers
Provable Low-Rank Tensor-Train Approximations in the Inverse of Large-Scale Structured Matrices
Chuanfu Xiao, Kejun Tang, Zhitao Zhu
This paper studies the low-rank property of the inverse of a class of large-scale structured matrices in the tensor-train (TT) format, which is typically discretized from different…
Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams
Cuiyu Liu, Chuanfu Xiao, Mingshuo Ding +1
Low-rank approximation in data streams is a fundamental and significant task in computing science, machine learning and statistics. Multiple streaming algorithms have emerged over…
A rank-adaptive higher-order orthogonal iteration algorithm for truncated Tucker decomposition
Chuanfu Xiao, Chao Yang
We propose a novel rank-adaptive higher-order orthogonal iteration (HOOI) algorithm to compute the truncated Tucker decomposition of higher-order tensors with a given error toleran…
a-Tucker: Input-Adaptive and Matricization-Free Tucker Decomposition for Dense Tensors on CPUs and GPUs
Min Li, Chuanfu Xiao, Chao Yang
Tucker decomposition is one of the most popular models for analyzing and compressing large-scale tensorial data. Existing Tucker decomposition algorithms usually rely on a single s…
Efficient Alternating Least Squares Algorithms for Low Multilinear Rank Approximation of Tensors
Chuanfu Xiao, Chao Yang, Min Li
The low multilinear rank approximation, also known as the truncated Tucker decomposition, has been extensively utilized in many applications that involve higher-order tensors. Popu…