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Fig. 11 An illustration of principal components analysis.
This allows us to build up a set of principal components of diminishing importance.
Aiming at reducing the complexity, we developed novel reduced-rank identification of principal components (RIPC) algorithms.
The rank is similar to number of principal components in PCA.
The number of principal components for KPCA and PCA were set to n = 40.
We use 0.90 as the threshold to determine the number of principal components to select.
Section 4.3 presents a test for checking homoscedasticity of principal components.
The varimax rotated factor loadings of principal components (PCs) are presented in Table 6.
The number of principal components corresponds to the data matrix rank.
The number of principal components to keep is determined based on a normalized energy threshold.
Therefore, different numbers of principal components are required for images with different features.
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of major components
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