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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 4 shows the principle component analysis results which present the revised TAM's validity.
The sentiment measures are computed via Principle Component Analysis as in Baker and Wurgler (2006).
Principle component analysis (PCA) is a data driven projection method.
All eighteen samples clustered tightly on principle component analysis (PCA) analysis (data not shown).
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.
Expression values for all probe sets were used to generate Principle Component Analysis (PCA) plots.
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