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Hybrid Pattern Recognition Using a Combination of Different Features
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 Title & Authors
Hybrid Pattern Recognition Using a Combination of Different Features
Choi, Sang-Il;
  PDF(new window)
 Abstract
We propose a hybrid pattern recognition method that effectively combines two different features for improving data classification. We first extract the PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) features, both of which are widely used in pattern recognition, to construct a set of basic features, and then evaluate the separability of each basic feature. According to the results of evaluation, we select only the basic features that contain a large amount of discriminative information for construction of the combined features. The experimental results for the various data sets in the UCI machine learning repository show that using the proposed combined features give better recognition rates than when solely using the PCA or LDA features.
 Keywords
Pattern classification;Feature extraction;Feature selection;Hybrid method;Discriminant analysis;Combined features;
 Language
Korean
 Cited by
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