Improved Defect Detection Using Novel Wavelet Feature Extraction Involving Principal Component Analysis and Neural Network Techniques.

2002 ◽  
pp. 638-647
Author(s):  
D.A. Karras ◽  
B.G. Mertzios
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Defect Detection ◽  
Principal Component ◽  
Component Analysis

scholarly journals Neural Network and Adaptive Feature Extraction Technique for Pattern Recognition

2011 ◽  
Vol 8 (1) ◽  
pp. 01-06
Author(s):  
Wael M. Khedr ◽  
Qamar A. Awad
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Back Propagation ◽  
Feature Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
Adaptive Feature Extraction

In this paper, we propose adaptive K-means algorithm upon the principal component analysis PCA feature extraction to pattern recognition by using a neural network model. Adaptive k-means to discriminate among objects belonging to different groups based upon the principal component analysis PCA implemented for statistical feature extraction. The features extracted by PCA consistently reduction dimensional algorithm, thus demonstrating that the suite of structure detectors effectively performs generalized feature extraction. The classification accuracies achieved using feature learning process of back propagation neural network . A comparison of the proposed adaptive and previous non-adaptive ensemble is the primary goal of the experiments. We evaluated the performance of the clustering ensemble algorithms by matching the detected and the known partitions of the iris dataset. The best possible matching of clusters provides a measure of performance expressed as the misassignment rate.

Sign in / Sign up

Export Citation Format

Share Document