activity
20192021
most citedRobust Tensor Completion Using Transformed Tensor SVD

9 citations · 10 across the 4 of their papers we have counts for

collaborators

9 papers

cs.CV2021

Fully-Connected Tensor Network Decomposition for Robust Tensor Completion Problem

Yun-Yang Liu, Xi-Le Zhao, Guang-Jing Song +2

The robust tensor completion (RTC) problem, which aims to reconstruct a low-rank tensor from partially observed tensor contaminated by a sparse tensor, has received increasing atte…

math.NA2020★ 1 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…

stat.ML2020

Tensor Completion by Multi-Rank via Unitary Transformation

Guang-Jing Song, Michael K. Ng, Xiongjun Zhang

One of the key problems in tensor completion is the number of uniformly random sample entries required for recovery guarantee. The main aim of this paper is to study $n_1 \times n_…

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.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

Nonnegative Low Rank Tensor Approximation and its Application to Multi-dimensional Images

Tai-Xiang Jiang, Michael K. Ng, Junjun Pan +1

The main aim of this paper is to develop a new algorithm for computing nonnegative low rank tensor approximation for nonnegative tensors that arise in many multi-dimensional imagin…