174 citations · 213 across the 10 of their papers we have counts for
10 papers
Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices
Jiangjun Peng, Yao Wang, Hongying Zhang +2
It is known that the decomposition in low-rank and sparse matrices (\textbf{L+S} for short) can be achieved by several Robust PCA techniques. Besides the low rankness, the local sm…
Effective Tensor Completion via Element-wise Weighted Low-rank Tensor Train with Overlapping Ket Augmentation
Yang Zhang, Yao Wang, Zhi Han +2
In recent years, there have been an increasing number of applications of tensor completion based on the tensor train (TT) format because of its efficiency and effectiveness in deal…
A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples
Jiapeng Liu, Milosz Kadzinski, Xiuwu Liao +2
We present a preference learning framework for multiple criteria sorting. We consider sorting procedures applying an additive value model with diverse types of marginal value funct…
RIP-based performance guarantee for low-tubal-rank tensor recovery
Feng Zhang, Wendong Wang, Jianwen Huang +2
The essential task of tensor data analysis focuses on the tensor decomposition and the corresponding notion of rank. In this paper, by introducing the notion of tensor Singular Val…
Enhanced 3DTV Regularization and Its Applications on Hyper-spectral Image Denoising and Compressed Sensing
Jiangjun Peng, Qi Xie, Qian Zhao +3
The 3-D total variation (3DTV) is a powerful regularization term, which encodes the local smoothness prior structure underlying a hyper-spectral image (HSI), for general HSI proces…
Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition
Yao Wang, Jiangjun Peng, Qian Zhao +3
Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, and…