Experimental Study: Influence of Feature Extraction in Objects Multiclassification

2018 ◽  
Author(s):  
Bouchra Honnit ◽  
Mohamed Nabil Saidi ◽  
Ahmed Tamtaoui
Keyword(s):  
Experimental Study ◽  
Feature Extraction

Experimental study of hydraulic cylinder leakage and fault feature extraction based on wavelet packet analysis

2015 ◽  
Vol 106 ◽  
pp. 33-40 ◽  
Author(s):  
Xiuxu Zhao ◽  
Shuanshuan Zhang ◽  
Chuanli Zhou ◽  
Zhemin Hu ◽  
Rui Li ◽  
...  
Keyword(s):  
Experimental Study ◽  
Feature Extraction ◽  
Wavelet Packet ◽  
Hydraulic Cylinder ◽  
Wavelet Packet Analysis ◽  
Fault Feature Extraction

Experimental Study of FPCA on its Generalization Performance in Image Classification

2014 ◽  
Vol 496-500 ◽  
pp. 2299-2302
Author(s):  
Ke Wang Huang
Keyword(s):  
Experimental Study ◽  
Feature Extraction ◽  
Sample Size ◽  
Performance Metrics ◽  
Theoretical Study ◽  
Experimental Studies ◽  
Performance Criterion ◽  
Large Sample Size ◽  
Theoretical Conclusion ◽  
Generalization Performance

The theoretical study of FPCA shows that FPCA algorithm has better generalization performance than existing PCA and its extended algorithms. But this theoretic conclusion was not confirmed by existing experimental results because of the problems of evaluation criterion. Introducing the idea of clustering performance criterion of LDA, we proposed a general performance metrics for PCA and performed numbers of experimental studies to compare FPCA with existing PCA and its extended algorithms by using our metrics. We found in the feature extraction of image samples that FPCA really has better generalization performance than existing PCA and its extended algorithms under the condition of large sample size. The results confirmed theoretical conclusion of FPCA and improved relevant experimental study.

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