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In this study, we have employed the sonograms depicted from Autoregressive (AR) modeling, Principles component analysis (PCA) for data reduction of Doppler sonograms and artificial neural networks (ANN) in order to distinguish between atherosclerosis and healthy subjects.
Principles component analysis focusing on the GeneChip control probes only demonstrates the lack of distinct populations of GeneChips caused by any experimental factors (Figure 8 B), providing evidence for the positive impact of the controlled reduction of variation introduced by technical parameters.
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Kernel principle component analysis.
principle component analysis.
Table 1 Eigenvectors from Principle Component Analysis.
That algorithm employs adaptive principle component analysis (PCA) [43].
Principle Component Analysis (PCA) is a mathematical procedure widely used for high dimensional data analysis.
Then, Principle Component Analysis (PCA) [63] is applied to these vectors as follows.
Each asset is assigned a weight based on the principle component analysis.
Principle component analysis (PCA) was used to identify and explore covariance patterns among meteorological factors.
Table 1 Result of principle component analysis (PCA) for the samples from the Kushida River Delta.
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