10 citations · 32 across the 9 of their papers we have counts for
10 papers
Towards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration
Yichun Qiu, Weijun Sun, Guoxu Zhou +1
Efficient and accurate low-rank approximation (LRA) methods are of great significance for large-scale data analysis. Randomized tensor decompositions have emerged as powerful tools…
Latent Matrices for Tensor Network Decomposition and to Tensor Completion
Peilin Yang, Weijun Sun, Qibin Zhao +1
The prevalent fully-connected tensor network (FCTN) has achieved excellent success to compress data. However, the FCTN decomposition suffers from slow computational speed when faci…
A high-order tensor completion algorithm based on Fully-Connected Tensor Network weighted optimization
Peilin Yang, Yonghui Huang, Yuning Qiu +2
Tensor completion aimes at recovering missing data, and it is one of the popular concerns in deep learning and signal processing. Among the higher-order tensor decomposition algori…
Noisy Tensor Completion via Low-rank Tensor Ring
Yuning Qiu, Guoxu Zhou, Qibin Zhao +1
Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to predict missing entries from partial observations. However, existing methods often make t…
Multi-view Data Classification with a Label-driven Auto-weighted Strategy
Yuyuan Yu, Guoxu Zhou, Haonan Huang +2
Distinguishing the importance of views has proven to be quite helpful for semi-supervised multi-view learning models. However, existing strategies cannot take advantage of semi-sup…
Fast Hypergraph Regularized Nonnegative Tensor Ring Factorization Based on Low-Rank Approximation
Xinhai Zhao, Yuyuan Yu, Guoxu Zhou +2
For the high dimensional data representation, nonnegative tensor ring (NTR) decomposition equipped with manifold learning has become a promising model to exploit the multi-dimensio…