2 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
Partially Shared Semi-supervised Deep Matrix Factorization with Multi-view Data
Haonan Huang, Naiyao Liang, Wei Yan +2
Since many real-world data can be described from multiple views, multi-view learning has attracted considerable attention. Various methods have been proposed and successfully appli…