18 citations · 18 across the 2 of their papers we have counts for
5 papers
Tensor Recovery Based on A Novel Non-convex Function Minimax Logarithmic Concave Penalty Function
Hongbing Zhang, Xinyi Liu, Chang Liu +3
Non-convex relaxation methods have been widely used in tensor recovery problems, and compared with convex relaxation methods, can achieve better recovery results. In this paper, a…
Low-Rank Tensor Completion Based on Bivariate Equivalent Minimax-Concave Penalty
Hongbing Zhang, Xinyi Liu, Hongtao Fan +2
Low-rank tensor completion (LRTC) is an important problem in computer vision and machine learning. The minimax-concave penalty (MCP) function as a non-convex relaxation has achieve…
Tensor Full Feature Measure and Its Nonconvex Relaxation Applications to Tensor Recovery
Hongbing Zhang, Xinyi Liu, Hongtao Fan +2
Tensor sparse modeling as a promising approach, in the whole of science and engineering has been a huge success. As is known to all, various data in practical application are often…
Deep neural network methods for solving forward and inverse problems of time fractional diffusion equations with conformable derivative
Yinlin Ye, Yajing Li, Hongtao Fan +2
Physics-informed neural networks (PINNs) show great advantages in solving partial differential equations. In this paper, we for the first time propose to study conformable time fra…
Two New Low Rank Tensor Completion Methods Based on Sum Nuclear Norm
Hongbing Zhang, Xinyi Liu, Hongtao Fan +2
The low rank tensor completion (LRTC) problem has attracted great attention in computer vision and signal processing. How to acquire high quality image recovery effect is still an…