Accelerating the kernel-method-based feature extraction procedure from the viewpoint of numerical approximation

2011 ◽  
Vol 20 (7) ◽  
pp. 1087-1096 ◽  
Author(s):  
Yong Xu ◽  
David Zhang
Keyword(s):  
Feature Extraction ◽  
Numerical Approximation ◽  
Kernel Method ◽  
Extraction Procedure

scholarly journals A new kernel method for hyperspectral image feature extraction

2017 ◽  
Vol 20 (4) ◽  
pp. 309-318 ◽  
Author(s):  
Bin Zhao ◽  
Lianru Gao ◽  
Wenzhi Liao ◽  
Bing Zhang
Keyword(s):  
Feature Extraction ◽  
Hyperspectral Image ◽  
Kernel Method ◽  
Image Feature ◽  
Image Feature Extraction

scholarly journals Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis

2016 ◽  
Vol 8 (12) ◽  
pp. 168781401668308 ◽  
Author(s):  
Shuangyuan Wang ◽  
Yixiang Huang ◽  
Liang Gong ◽  
Lin Li ◽  
Chengliang Liu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Sparse Coding ◽  
Extraction Procedure ◽  
Vibration Signal ◽  
Support Vector ◽  
Data Set ◽  
Vibration Signals ◽  
Efficiency And Effectiveness ◽  
Gear Fault

Vibration signals reflecting different kinds of machinery conditions are very useful for fault diagnosis. However, vibration signal characteristics are not the same for different types of equipment and patterns of failure. This available information is often lost in structureless condition diagnosis models. We propose a structured Fisher discrimination sparse coding–based fault diagnosis scheme to improve the feature extraction procedure considering both efficiency and effectiveness. There are three major components: (1) a structured dictionary for synthesizing the vibration signals that is learned by structure Fisher discrimination dictionary learning, (2) a tree-structured sparse coding to extract sparse representation coefficients from vibration signals to represent fault features, and (3) a support vector machine’s classifier on the features to recognize different faults. The proposed algorithm is verified on a standard bearing fault data set and a worm gear fault experiment. Test results have proved that the proposed method can achieve better performance with considerable efficiency and generalization ability.

scholarly journals Solving Delay Differential Equations by an Accurate Method with Interpolation

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Ali Akgül ◽  
Adem Kiliçman
Keyword(s):  
Differential Equations ◽  
Delay Differential Equations ◽  
Numerical Approximation ◽  
Reproducing Kernel ◽  
Kernel Method ◽  
Approximate Solutions ◽  
Accurate Method ◽  
Reproducing Kernel Method ◽  
Comparison Of The Results ◽  
Delay Differential

We use the reproducing kernel method (RKM) with interpolation for finding approximate solutions of delay differential equations. Interpolation for delay differential equations has not been used by this method till now. The numerical approximation to the exact solution is computed. The comparison of the results with exact ones is made to confirm the validity and efficiency.

scholarly journals Relative brain signature: a population‐based feature extraction procedure to identify functional biomarkers in the brain of alcoholics

2015 ◽  
Vol 5 (7) ◽  
Author(s):  
Nader Karamzadeh ◽  
Yasaman Ardeshirpour ◽  
Matthew Kellman ◽  
Fatima Chowdhry ◽  
Afrouz Anderson ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Procedure ◽  
Population Based ◽  
The Brain ◽  
Functional Biomarkers

Multi-classification of action intention understanding brain signals based on thresholding graph metric

2021 ◽  
pp. 1-8
Author(s):  
Qian Cai ◽  
Xingliang Xiong ◽  
Weiqiang Gong ◽  
Haixian Wang
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Binary Classification ◽  
Extraction Procedure ◽  
Graph Metrics ◽  
Brain Signals ◽  
Intention Understanding ◽  
Multi Classification ◽  
Action Intention

BACKGROUND: Classification of action intention understanding is extremely important for human computer interaction. Many studies on the action intention understanding classification mainly focus on binary classification, while the classification accuracy is often unsatisfactory, not to mention multi-classification. METHOD: To complete the multi-classification task of action intention understanding brain signals effectively, we propose a novel feature extraction procedure based on thresholding graph metrics. RESULTS: Both the alpha frequency band and full-band obtained considerable classification accuracies. Compared with other methods, the novel method has better classification accuracy. CONCLUSIONS: Brain activity of action intention understanding is closely related to the alpha band. The new feature extraction procedure is an effective method for the multi-classification of action intention understanding brain signals.

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