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Lerner et al.:Feature Extraction by NN Nonlinear Mapping 1 Feature Extraction by Neural Network Nonlinear Mapping
 

Summary: Lerner et al.:Feature Extraction by NN Nonlinear Mapping 1
1
Feature Extraction by Neural Network Nonlinear Mapping
for Pattern Classification
B. Lerner, H. Guterman, M. Aladjem, and I. Dinstein
Department of Electrical and Computer Engineering
Ben-Gurion University of the Negev
Beer-Sheva 84105, Israel
Abstract
Feature extraction has been always mutually studied for exploratory data projection and for
classification. Feature extraction for exploratory data projection aims for data visualization by a
projection of a high-dimensional space onto two or three-dimensional space, while feature
extraction for classification generally requires more than two or three features. Therefore, feature
extraction paradigms for exploratory data projection are not commonly employed for
classification and vice versa. We study extraction of more than three features, using neural
network (NN) implementation of Sammon's nonlinear mapping to be applied for classification.
Comparative classification experiments reveal that Sammon's method, which is primarily an
exploratory data projection technique, has a remarkable classification capability. The classification
performance of (the unsupervized) Sammon's mapping is highly comparable with the performance
of the principal component analysis (PCA) based feature extractor and is slightly inferior to the

  

Source: Aladjem, Mayer - Department of Electrical and Computer Engineering, Ben-Gurion University
Lerner, Boaz - Department of Electrical and Computer Engineering, Ben-Gurion University

 

Collections: Computer Technologies and Information Sciences