scholarly journals Dictionary Learning via a Mixed Noise Model for Sparse Representation Classification of Rolling Bearings

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 213416-213425
Author(s):  
Jialing Zhang ◽  
Jimei Wu ◽  
Bingbing Hu
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Noise Model ◽  
Rolling Bearings ◽  
Sparse Representation Classification ◽  
Mixed Noise

scholarly journals AIRBORNE LIDAR POINTS CLASSIFICATION BASED ON TENSOR SPARSE REPRESENTATION

2017 ◽  
Vol IV-2/W4 ◽  
pp. 107-114
Author(s):  
N. Li ◽  
N. Pfeifer ◽  
C. Liu
Keyword(s):  
Sparse Representation ◽  
Statistical Methods ◽  
Dictionary Learning ◽  
Supervised Classification ◽  
Airborne Lidar ◽  
Training Data ◽  
Sparse Representation Classification ◽  
The Common

The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. This paper proposes a tensor sparse representation classification (SRC) method for airborne LiDAR points. The LiDAR points are represented as tensors to keep attributes in its spatial space. Then only a few of training data is used for dictionary learning, and the sparse tensor is calculated based on tensor OMP algorithm. The point label is determined by the minimal reconstruction residuals. Experiments are carried out on real LiDAR points whose result shows that objects can be distinguished by this algorithm successfully.

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