Earticle

초록

영어

In recent years, tensor completion problem has received a significant amount of attention in computer vision, data mining and neuroscience. It is the higher order generalization of matrix completion. And these can be solved by the convex relaxation which minimizes the tensor nuclear norm instead of the n-rank of the tensor. In this paper, we introduce the weighted nuclear norm for tensor and develop majorization-minimization weighted soft thresholding algorithm to solve it. Focusing on the tensors generated randomly and image inpainting problems, our proposed algorithm experimentally shows a significant improvement with respect to the accuracy in comparison with the existing algorithm HaLRTC.

목차

Abstract
 1. Introduction
 2. Notations and Preliminaries on Tensor Completion
  2.1 Preliminaries on Tensor
  2.1 Low n-rank Tensor completion Problem
 3. Proposed tensor Completion Algorithm
 4. Numerical Experiments
  4.1 Sysnthetic data
  4.2 Image Simulation
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Juan Geng [ College of Science, China Agricultural University 100083 Beijing, China, College of Mathematics and Statistics, Hebei University of Economics and Business 050064 Shijiazhuang, China ]
• Laisheng Wang [ College of Science, China Agricultural University 100083 Beijing, China ]
• Yitian Xu [ College of Science, China Agricultural University 100083 Beijing, China ]
• Xiuyu Wang [ Shijiazhuang Information Engineering Vacational College 050035 Shijiazhuang, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605