.png){kind=icon}
返回
Low-Rank tensor completion based on nonconvex regularization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2023.109157.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we consider the low-rank tensor completion which aims to exactly recover incomplete high-dimensional visual data. Existing studies utilize widely tensor nuclear norm minimization (TNNM), a convex relaxation to tensor-rank minimization (TRM), to solve tensor completion tasks. Nevertheless, TNNM ignores the difference between different tensor singular values induced by the tensor singular value decompositions (t-SVD) and then the obtained solution may be suboptimal. In this paper, we pro- pose a nonconvex minimization approach to solve the tensor completion problem more effectively by adopting a nonconvex regularization to further approximate the tensor-rank. Moreover, alternating direc- tion method of multipliers (ADMM) and iteratively reweighted nuclear norm (IRNN) are adopted to solve the constructed nonconvex models efficiently, and the convergence can also be guaranteed. Finally, we present that the proposed nonconvex optimization methods are suitable for solving other TRM problems induced by any invertible linear transform, such as subspace clustering based on low-rank representa- tion. Extensive experiments on real images and videos validate the superiority of our approach over the state-of-the-art algorithms.& COPY; 2023 Elsevier B.V. All rights reserved.
Keyword:
Tensor completion
Nonconvex tensor nuclear norm
Tensor singular value decomposition
Low-rank
期刊
IF:
3.6
论文数:
9.9K
被引数:
1.7W
机构
引用论文
Identification of the enhancer binding protein MBF-1 of the sea urchin modulator α-H2A histone gene
PREAI
分享收藏分享收藏DC Offset Error Compensation Algorithm for PR Current Control of a Single-Phase Grid-Tied Inverter.png)
Energies
OAAI
EnergiesIF0
OAAI分享收藏Learning robust and discriminative low-rank representations for face recognition with occlusion学习鲁棒和有判别力的低秩表示，用于具有遮挡的人脸识别PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Tensor completion via nonconvex tensor ring rank minimization with guaranteed convergenceSIGNAL PROCESSING
OAAI
SIGNAL PROCESSINGIF3.6
OAAI分享收藏
查看更多内容