Pattern recognition in noisy environment using principal component analysis and spectral angle mapping

2006 ◽  
Author(s):  
Z. Boz ◽  
M. S. Alam ◽  
E. Sarigul
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Noisy Environment ◽  
Spectral Angle

Pattern Recognition on X-ray Fluorescence Records from Copenhagen Lake Sediments Using Principal Component Analysis

2014 ◽  
Vol 225 (12) ◽  
Author(s):  
Norman Schreiber ◽  
Emanuel Garcia ◽  
Aart Kroon ◽  
Peter C. Ilsøe ◽  
Kurt H. Kjær ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Lake Sediments ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray

scholarly journals PENGENALAN EKSPRESI RAUT WAJAH BERBASIS JARINGAN SARAF TIRUAN BACKPROPAGATION DENGAN METODE PRINCIPAL COMPONENT ANALYSIS

2021 ◽  
Vol 15 (1) ◽  
pp. 037-046
Author(s):  
Harizahayu Harizahayu
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Learning Rate ◽  
Neural Network Models ◽  
Biometric Analysis ◽  
Facial Pattern ◽  
Pca Method ◽  
Artificial Neural

The development of artificial neural networks is related to statistical and biometric analysis which is one of the applications that can require artificial neural network models. Recognition of facial patterns is an important part of identifying a person. The face can be divided into areas such as the nose, eyes and mouth. Face pattern recognition is a research area that can be applied to the principal component analysis (PCA) method. The training process carried out by the eigenface calculation uses PCA and the results of this study show that facial pattern recognition based on the proportion of memorization and generalization for the use of the method without PCA is better than facial pattern recognition using PCA. Pattern recognition without using the PCA method, the level of memorization and generalization reaches 100% at the 40th iteration and 0.0099 error with a learning rate and momentum of 0.8, while facial pattern recognition using the PCA method, the memorization and generalization level reaches 100% in the iteration. to -1000 and error 0.00103 with learning rate and momentum 0.9.

scholarly journals Temperature extraction in Brillouin optical time-domain analysis sensors using principal component analysis based pattern recognition

2017 ◽  
Vol 25 (14) ◽  
pp. 16534 ◽  
Author(s):  
Abul Kalam Azad ◽  
Faisal Nadeem Khan ◽  
Waled Hussein Alarashi ◽  
Nan Guo ◽  
Alan Pak Tao Lau ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Time Domain ◽  
Principal Component ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Component Analysis ◽  
Optical Time
Sign in / Sign up

Export Citation Format

Share Document