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.

scholarly journals Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1263
Author(s):  
Chih-Yao Chang ◽  
Kuo-Ping Lin
Keyword(s):  
Support Vector Machine ◽  
Data Quality ◽  
Classification Performance ◽  
Patent Infringement ◽  
Classification Method ◽  
Training Dataset ◽  
Support Vector ◽  
Classification Problems ◽  
Data Quality Management ◽  
Patent Rights

Classification problems are very important issues in real enterprises. In the patent infringement issue, accurate classification could help enterprises to understand court decisions to avoid patent infringement. However, the general classification method does not perform well in the patent infringement problem because there are too many complex variables. Therefore, this study attempts to develop a classification method, the support vector machine with new fuzzy selection (SVMFS), to judge the infringement of patent rights. The raw data are divided into training and testing sets. However, the data quality of the training set is not easy to evaluate. Effective data quality management requires a structural core that can support data operations. This study adopts new fuzzy selection based on membership values, which are generated from fuzzy c-means clustering, to select appropriate data to enhance the classification performance of the support vector machine (SVM). An empirical example based on the SVMFS shows that the proposed SVMFS can obtain a superior accuracy rate. Moreover, the new fuzzy selection also verifies that it can effectively select the training dataset.

scholarly journals Aircraft Target Classification Method for Conventional Narrowband Radar Based on Micro-Doppler Effect

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Saiqiang Xia ◽  
Chaowei Zhang ◽  
Wanyong Cai ◽  
Jun Yang ◽  
Liangfa Hua ◽  
...  
Keyword(s):  
Doppler Effect ◽  
Classification Problem ◽  
Classification Performance ◽  
Central Moment ◽  
Classification Method ◽  
Support Vector ◽  
Machine Model ◽  
Different Types ◽  
Characteristics Analysis ◽  
The Time Domain

For a conventional narrowband radar system, its insufficient bandwidth usually leads to the lack of detectable information of the target, and it is difficult for the radar to classify the target types, such as rotor helicopter, propeller aircraft, and jet aircraft. To address the classification problem of three different types of aircraft target, a joint multifeature classification method based on the micro-Doppler effect in the echo caused by the target micromotion is proposed in this paper. Through the characteristics analysis of the target simulation echoes obtained from the target scattering point model, four features with obvious distinguishability are extracted from the time domain and frequency domain, respectively, that is, flicker interval, fractal dimension, modulation bandwidth, and second central moment. Then, a support vector machine model will be applied to the classification of the three different types of aircraft. Compared with the conventional method, the proposed method has better classification performance and can significantly improve the classification probability of aircraft target. The simulations are carried out to validate the effectiveness of the proposed method.

A Novel Sparse Representation and Softmax Method for Human Activity Identification in Healthcare Systems

2020 ◽  
Vol 10 (7) ◽  
pp. 1724-1733
Author(s):  
Youwei Yuan ◽  
Wenpeng Tao ◽  
Jintao Zhang ◽  
Meilian Zheng ◽  
Yao Yao ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Human Activity ◽  
Nearest Neighbor ◽  
Feature Space ◽  
Healthcare Systems ◽  
Classification Performance ◽  
Identification Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Research Attention

Human activity identification has been attracting extensive research attention due to its prominent applications in healthcare systems such as healthcare monitoring and rehabilitation process. Traditional methods are greatly dependent on hand-crafted feature extraction, hampering their generalization performance. In this research, a novel sparse representation and softmax (SRS) method is presented for human activity identification to reduce the computation complexity of the task and improve the accuracy of classification. The multi-class classifier based on the softmax function is firstly introduced to improve sensor data classification performance. Sparse representation technology is then applied in our work to extract human activity features from sensor data. The output of the classifier model, taking raw sensor data after transforming into a high-dimensional feature space as input, provides a normalization of the probability distribution of activity categories, thereby ensuring accuracy and efficiency under diverse human activities. Experiments on a collection of raw sensor data from wireless sensor networks demonstrate the identification accuracy of our approach compared with nearest neighbor, naive Bayesian classifier, and support vector machine methods. The F1-score of the proposed method is respectively 14.1%, 19.6%, and 6.8% higher than the approaches mentioned above, indicating the effectiveness of SRS.

Robust patch-based sparse representation for hyperspectral image classification

2017 ◽  
Vol 15 (03) ◽  
pp. 1750028 ◽  
Author(s):  
Haoliang Yuan
Keyword(s):  
Sparse Representation ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Real Life ◽  
Test Sample ◽  
Classification Performance ◽  
Iteration Algorithm ◽  
Frobenius Norm ◽  
Training Samples ◽  
Sparse Representation Classification

Sparse representation classification (SRC) has been successfully applied into hyperspectral image (HSI). A test sample (pixel) can be linearly represented by a few training samples of the training set. The class label of the test sample is then decided by the reconstruction residuals. To incorporate the spatial information to improve the classification performance, a patch matrix, which includes a spatial neighborhood set, is used to replace the original pixel. Generally, the objective function of the reconstruction residuals is represented as Frobenius-norm, which actually treats the elements in the reconstruction residuals in the same way. However, when a patch locates in the image edge, the samples in the patch may belong to different classes. Frobenius-norm is not suitable to compute the reconstruction residuals. In this paper, we propose a robust patch-based sparse representation classification (RPSRC) based on [Formula: see text]-norm. An iteration algorithm is given to compute RPSRC efficiently. Extensive experimental results on two real-life HSI datasets demonstrate the effectiveness of RPSRC.

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