.png){kind=icon}
返回
Tensor low-rank sparse representation for tensor subspace learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2021.02.002.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Many subspace learning methods are implemented on a matrix of sample data. For multi-dimensional data, these methods have to convert data samples into vectors in advance, which often destroys the inherent spatial structure of the sample data. In this paper, we propose a robust tensor low-rank sparse representation (TLRSR) method that can directly perform subspace learning on three-dimensional tensors. Firstly, the dual constraints of low-rankness and sparseness make the representation tensor effectively capture the global structure and local structure of sample data, respectively. Secondly, in order to deal with outliers and noise, we adopt the tensor l(2,1)-norm to characterize the noise of tensor composed of multiple samples. Thirdly, the denoised tensor instead of the original tensor is used as the dictionary to find the low-rank sparse representation tensor. Finally, an iterative update algorithm is proposed for the optimization of TLRSR, compared with the state-of-the-art methods, clustering on face images and denoising on real images verify the good performance of our proposed TLRSR in tensor subspace learning. (C) 2021 Elsevier B.V. All rights reserved.
Keyword:
Tensor subspace learning
Low-rank representation
Sparse representation
Tensor l(2,1)-norm
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
DC Offset Error Compensation Algorithm for PR Current Control of a Single-Phase Grid-Tied Inverter.png)
Energies
OAAI
EnergiesIF0
OAAI分享收藏分享收藏Tinnitus Retraining Therapy (TRT) as a Method for Treatment of Tinnitus and Hyperacusis Patients耳鸣再训练疗法 (TRT) 作为治疗耳鸣和高亢患者的方法
PREAI
分享收藏
查看更多内容