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20172021
most citedA parallel-in-time two-sided preconditioning for all-at-once system from a non-local evolutionary equation with weakly singular kernel

20 citations · 54 across the 15 of their papers we have counts for

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Showing 2020Show all

10 papers · 1 filter

math.NA20201 cited

Low Rank Pure Quaternion Approximation for Pure Quaternion Matrices

Guangjing Song, Weiyang Ding, Michael K. Ng

Quaternion matrices are employed successfully in many color image processing applications. In particular, a pure quaternion matrix can be used to represent red, green and blue chan…

math.OC2020

Riemannian Conjugate Gradient Descent Method for Third-Order Tensor Completion

Guang-Jing Song, Xue-Zhong Wang, Michael K. Ng

The goal of tensor completion is to fill in missing entries of a partially known tensor under a low-rank constraint. In this paper, we mainly study low rank third-order tensor comp…

cs.CV2020

Image Denoising by Gaussian Patch Mixture Model and Low Rank Patches

Jing Guo, Shuping Wang, Chen Luo +2

Non-local self-similarity based low rank algorithms are the state-of-the-art methods for image denoising. In this paper, a new method is proposed by solving two issues: how to impr…

cs.LG2020

Tangent Space Based Alternating Projections for Nonnegative Low Rank Matrix Approximation

Guangjing Song, Michael K. Ng, Tai-Xiang Jiang

In this paper, we develop a new alternating projection method to compute nonnegative low rank matrix approximation for nonnegative matrices. In the nonnegative low rank matrix appr…

cs.CV2020

Dictionary Learning with Low-rank Coding Coefficients for Tensor Completion

Tai-Xiang Jiang, Xi-Le Zhao, Hao Zhang +1

In this paper, we propose a novel tensor learning and coding model for third-order data completion. Our model is to learn a data-adaptive dictionary from the given observations, an…

cs.CV2020

Tensor train rank minimization with nonlocal self-similarity for tensor completion

Meng Ding, Ting-Zhu Huang, Xi-Le Zhao +2

The tensor train (TT) rank has received increasing attention in tensor completion due to its ability to capture the global correlation of high-order tensors ().…