.png){kind=icon}
返回
Dual low-rank structure embedding for robust visual information processing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2024.111821.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The low -rank (LR) property is widely applied to capture the global as well as intrinsic structure of the given data in different visual information processing tasks. Actually, there are three key information to determine the performance and generalization low -rank property based methods: (1) visual intrinsic structural information, (2) visual representation structural information, (3) visual robust information. To achieve these jointly in a unified framework, in this paper, we propose D ual L ow -rank S tructure E mbedding (DLSE) that embeds structural and robust information. We additionally proposed the J oint M atrix -based L inear R epresentation (JMLR) and theoretically proved it can realize DLSE. The proposed method was validated on 6 datasets (from 1,440 samples to 70,000 samples in size) and showed a promising performance in visual recognition (14.32% improvement compared with the deep features). In addition, we performed multiple analysis on robustness, representation, and its parameters to show the effectiveness of DLSE from different aspects.
Keyword:
Low rank
Structure embedding
Bayesian inference
Visual information processing
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Observation and Prediction of the Effects of Water Chemistry and Mechanics on Environmentally Assisted Cracking of Inconels 182 Weld Metal and 600.png)
CORROSION
CORROSIONIF0
PREAI
分享收藏Efficient classification with sparsity augmented collaborative representationPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
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
查看更多内容