.png){kind=icon}
Return
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}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
Low rank
Structure embedding
Bayesian inference
Visual information processing
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
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
ShareSaveEfficient classification with sparsity augmented collaborative representationPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveShareSave
ShareSaveLearning robust and discriminative low-rank representations for face recognition with occlusionPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveShareSave
View more