文章基本信息

标题：A Weighted Nuclear Norm Method for Tensor Completion
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作者：Juan Geng ; Laisheng Wang ; Yitian Xu 等
期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2014
卷号：7
期号：1
页码：1-12
DOI：10.14257/ijsip.2014.7.1.01
出版社：SERSC
摘要：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.
关键词：Tensor completion; weighted nuclear norm; majorization-minimization method; ; image inpainting