scholarly journals Classification of Structural Images via High-Dimensional Image Warping, Robust Feature Extraction, and SVM

2005 ◽  
pp. 1-8 ◽  
Author(s):  
Yong Fan ◽  
Dinggang Shen ◽  
Christos Davatzikos
Keyword(s):  
Feature Extraction ◽  
Image Warping ◽  
High Dimensional ◽  
Dimensional Image ◽  
Robust Feature Extraction

Feature Extraction in Remote Sensing High-Dimensional Image Data

2007 ◽  
Vol 4 (1) ◽  
pp. 107-111 ◽  
Author(s):  
Maciel Zortea ◽  
Victor Haertel ◽  
Robin Clarke
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Image Data ◽  
High Dimensional ◽  
Dimensional Image

A Robust Feature Extraction Algorithm for the Classification of Acoustic Targets in Wild Environments

2015 ◽  
Vol 34 (7) ◽  
pp. 2395-2406 ◽  
Author(s):  
Jingchang Huang ◽  
Shiliang Xiao ◽  
Qianwei Zhou ◽  
Feng Guo ◽  
Xing You ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Robust Feature Extraction

scholarly journals Feature Extraction and Classification of High Dimensional Biomedical Spectral Data

2009 ◽  
Vol 19 (3) ◽  
pp. 297-303 ◽  
Author(s):  
Jae-Hoon Cho ◽  
Jin-Il Park ◽  
Dae-Jong Lee ◽  
Myung-Geun Chun
Keyword(s):  
Feature Extraction ◽  
Spectral Data ◽  
High Dimensional

Robust feature extraction for rapid classification of damage in composites

2009 ◽  
Author(s):  
Clyde K. Coelho ◽  
Whitney Reynolds ◽  
Aditi Chattopadhyay
Keyword(s):  
Feature Extraction ◽  
Robust Feature Extraction

scholarly journals Feature extraction and artificial neural networks for the on-the-fly classification of high-dimensional thermochemical spaces in adaptive-chemistry simulations

2021 ◽  
Vol 2 ◽  
Author(s):  
Giuseppe D’Alessio ◽  
Alberto Cuoci ◽  
Alessandro Parente
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Artificial Neural Networks ◽  
Laminar Flame ◽  
Principal Component ◽  
Precursor Chemistry ◽  
High Dimensional ◽  
Adaptive Simulation ◽  
Artificial Neural

Abstract The integration of Artificial Neural Networks (ANNs) and Feature Extraction (FE) in the context of the Sample- Partitioning Adaptive Reduced Chemistry approach was investigated in this work, to increase the on-the-fly classification accuracy for very large thermochemical states. The proposed methodology was firstly compared with an on-the-fly classifier based on the Principal Component Analysis reconstruction error, as well as with a standard ANN (s-ANN) classifier, operating on the full thermochemical space, for the adaptive simulation of a steady laminar flame fed with a nitrogen-diluted stream of n-heptane in air. The numerical simulations were carried out with a kinetic mechanism accounting for 172 species and 6,067 reactions, which includes the chemistry of Polycyclic Aromatic Hydrocarbons (PAHs) up to C $ {}_{20} $ . Among all the aforementioned classifiers, the one exploiting the combination of an FE step with ANN proved to be more efficient for the classification of high-dimensional spaces, leading to a higher speed-up factor and a higher accuracy of the adaptive simulation in the description of the PAH and soot-precursor chemistry. Finally, the investigation of the classifier’s performances was also extended to flames with different boundary conditions with respect to the training one, obtained imposing a higher Reynolds number or time-dependent sinusoidal perturbations. Satisfying results were observed on all the test flames.

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