Loading...
Please wait, while we are loading the content...
A Weighted Nuclear Norm Method for Tensor Completion
| Content Provider | Semantic Scholar |
|---|---|
| Author | Geng, Juan Wang, Laisheng Xu, Yitian Wang, Xiuyu |
| Copyright Year | 2014 |
| Abstract | 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.sersc.org/journals/IJSIP/vol7_no1/1.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Approximation error Bronchiolitis Obliterans Call of Duty: Black Ops Computer vision Copyright Data mining Eighty Experiment Generalization (Psychology) Harm Reduction Image processing Inpainting Iontophoresis Linear programming relaxation Lobular Neoplasia Local Interconnect Network Lupus erythematosus cell MM algorithm Neuroscience discipline Randomness Software release life cycle Synthetic data Technetium Thresholding (image processing) |
| Content Type | Text |
| Resource Type | Article |
