원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.7 No.1
2014.02
pp.1-12
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보