A Novel Weighted Sparse Representation Classification Strategy Based on Dictionary Learning for Rotating Machinery

2020 ◽  
Vol 69 (3) ◽  
pp. 712-720 ◽  
Author(s):  
Huaqing Wang ◽  
Bangyue Ren ◽  
Liuyang Song ◽  
Lingli Cui
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Rotating Machinery ◽  
Sparse Representation Classification ◽  
Classification Strategy

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.

scholarly journals Active Sonar Target Classification Method Based on Fisher’s Dictionary Learning

2021 ◽  
Vol 11 (22) ◽  
pp. 10635
Author(s):  
Tongjing Sun ◽  
Jiwei Jin ◽  
Tong Liu ◽  
Jun Zhang
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Classification Performance ◽  
Classification Method ◽  
Support Vector ◽  
Target Classification ◽  
Active Sonar ◽  
Reconstructed Signal ◽  
Sparse Representation Classification ◽  
Value Decomposition

The marine environment is complex and changeable, and the interference of noise and reverberation seriously affects the classification performance of active sonar equipment. In particular, when the targets to be measured have similar characteristics, underwater classification becomes more complex. Therefore, a strong, recognizable algorithm needs to be developed that can handle similar feature targets in a reverberation environment. This paper combines Fisher’s discriminant criterion and a dictionary-learning-based sparse representation classification algorithm, and proposes an active sonar target classification method based on Fisher discriminant dictionary learning (FDDL). Based on the learning dictionaries, the proposed method introduces the Fisher restriction criterion to limit the sparse coefficients, thereby obtaining a more discriminating dictionary; finally, it distinguishes the category according to the reconstruction errors of the reconstructed signal and the signal to be measured. The classification performance is compared with the existing methods, such as SVM (Support Vector Machine), SRC (Sparse Representation Based Classification), D-KSVD (Discriminative K-Singular Value Decomposition), and LC-KSVD (label-consistent K-SVD), and the experimental results show that FDDL has a better classification performance than the existing classification methods.

scholarly journals Multi-task hybrid dictionary learning for vehicle classification in sensor networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771880902
Author(s):  
Rui Wang ◽  
Miaomiao Shen ◽  
Tao Wang ◽  
Wenming Cao
Keyword(s):  
Sensor Networks ◽  
Sparse Representation ◽  
Dictionary Learning ◽  
Sparse Coding ◽  
Classification Accuracy ◽  
Time Complexity ◽  
Vehicle Classification ◽  
Support Vector ◽  
Heterogeneous Sensors ◽  
Sparse Representation Classification

In this article, we propose a novel multi-task hybrid dictionary learning approach for moving vehicle classification tasks using multi-sensor networks to improve the classification accuracy in complex scenes with low time complexity, which considers both correlations and complementary information among multiple heterogeneous sensors simultaneously to learn a hybrid dictionary within observations of each sensor. The efficient hybrid dictionary consists of a synthesis dictionary and an analysis dictionary, where discriminative codes can be generated by the trained analysis dictionary and class-specific discriminative reconstruction can be achieved by the trained synthesis dictionary. Extensive experiments are conducted on real data sets captured by the multiple heterogeneous sensors, and the results demonstrate that the proposed method can use the multi-feature fusion method to improve the vehicle classification accuracy, and it can learn a hybrid dictionary to make sure that the sparse coding matrix is obtained by simple linear mapping function. Moreover, the problem of [Formula: see text]-norm[Formula: see text] sparse coding can been solved, to reduce the time complexity of this algorithm, compared with support vector machine, sparse representation classification, label consistent KSVD, Fisher discrimination dictionary learning, hybrid dictionary learning, multi-task sparse representation classification, and multi-task Fisher discrimination dictionary learning algorithms.

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