26 citations · 75 across the 6 of their papers we have counts for
7 papers
Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction
Longhao Yuan, Chao Li, Jianting Cao +1
Dimensionality reduction is an essential technique for multi-way large-scale data, i.e., tensor. Tensor ring (TR) decomposition has become popular due to its high representation ab…
Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion
Longhao Yuan, Chao Li, Danilo Mandic +2
In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from the laborious model selection problem due to their high model sensitivity. In particula…
Higher-dimension Tensor Completion via Low-rank Tensor Ring Decomposition
Longhao Yuan, Jianting Cao, Qiang Wu +1
The problem of incomplete data is common in signal processing and machine learning. Tensor completion algorithms aim to recover the incomplete data from its partially observed entr…
Rank Minimization on Tensor Ring: A New Paradigm in Scalable Tensor Decomposition and Completion
Longhao Yuan, Chao Li, Danilo Mandic +2
In low-rank tensor completion tasks, due to the underlying multiple large-scale singular value decomposition (SVD) operations and rank selection problem of the traditional methods,…
High-dimension Tensor Completion via Gradient-based Optimization Under Tensor-train Format
Longhao Yuan, Qibin Zhao, Lihua Gui +1
Tensor train (TT) decomposition has drawn people's attention due to its powerful representation ability and performance stability in high-order tensors. In this paper, we propose a…
High-order Tensor Completion for Data Recovery via Sparse Tensor-train Optimization
Longhao Yuan, Qibin Zhao, Jianting Cao
In this paper, we aim at the problem of tensor data completion. Tensor-train decomposition is adopted because of its powerful representation ability and linear scalability to tenso…