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principle component analysis.
Kernel principle component analysis.
Table 1 Eigenvectors from Principle Component Analysis.
That algorithm employs adaptive principle component analysis (PCA) [43].
Table 2 shows the contribution rate of every principle component.
Principle component analysis (PCA) is a data driven projection method.
Principle component analyses were performed with the smartPCA software [45].
All eighteen samples clustered tightly on principle component analysis (PCA) analysis (data not shown).
The major component, principle component 1, represented 33.5 percent of the variability.
Principle component analysis (PCA) was performed using the R software (http://www.r-project.org/; [61]).
Next, principle component analysis (PCA) was used to examine the drivers for variability within the dataset.
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