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After extraction, the extracted components were transferred to a holding tank and simultaneously injected into the GC column also using the Hadamard-injector.
4. PPKNN uses PCA+PLS as feature extraction methods, all the newly extracted components are input into kNN. 5. GAPCAKNN uses PCA as feature extraction methods to extract new components from original gene set and GA as feature selection methods to select feature subset from the newly extracted components, the selected subset is input into kNN.
4. PPSVM uses PCA+PLS as feature extraction methods, all the newly extracted components are input into SVM. 5. GAPCASVM uses PCA as feature extraction methods to extract new components from original gene set and GA as feature selection methods to select feature subset from the newly extracted components, the selected subset is input into SVM.
2. PCAKNN uses PCA as feature extraction methods, all the newly extracted components are input into kNN.
2. PCASVM uses PCA as feature extraction methods, all the newly extracted components are input into SVM.
3. PLSSVM uses PLS as feature extraction methods, all the newly extracted components are input into SVM.
3. PLSKNN uses PLS as feature extraction methods, all the newly extracted components are input into kNN.
Centering decreases the absolute standard deviations of the extracted components.
Non-centering can, and probably will, break the premise of discorrelation between the extracted components.
High communalities indicate that the extracted components represent the variables well.
Also by using this method the extracted components will be invariant to speaker variations.
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