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The panel on the lower left corner shows the distributions of variances of principle components.
To find the optimal number of principle components for projection, all parameters were held constant while varying the number of principle components and applying a 10-fold cross-validation test.
Figure 3 Variations in classification accuracies versus number of principle components with respect to various classifiers for epilepsy diagnosis.
It can be seen that the optimal number of principle components to be used for projection is 256.
The results show that projecting a small number of principle components, such as 64, results in a large decrease in runtime but a comparatively lower recognition accuracy.
The parameters of the kernel PCA models, such as the number of principle components and the width of the Gaussian kernel, were fixed during the experiments.
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Table 1 Result of principle component analysis (PCA) for the samples from the Kushida River Delta.
Open image in new window Fig. 4 Factor score map of principle component (PC1) Open image in new window Fig. 5 Factor score map of principle component (PC2) Open image in new window Fig. 6 Factor score map of principle component (PC3) Open image in new window Fig. 7 Factor score map of principle component (PC4) Open image in new window Fig. 8 Factor score map of principle component (PC5).
In addition, leachate with different ages was categorized into 3 phases according to the results of Principle component analysis (PCA).
This study presents the first use of principle component analysis for identifying and successfully predicting the most important variables in the process of granulation and tabletting.
Wallis (1965) in discussion of multivariate statistical methods in hydrology recommends, for multifactor hydrological problems, the use of principle component analysis with varimax rotation of the factor weight matrix.
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