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The principal components have better explaining power compared to the single attributes.
The three principal components have accounted for approximately 79% of the total variance in the hydrochemical data.
In Fig. 6, the cross-PCA score as a function of the number of principal components have been plotted.
The first six principal components have eigenvalues above one and explain together more than 80%% of the total variance (Table 2).
(b) Eigenvalues: The variance or eigenvalue of the principal components given in Table 4 reveals that, the first 14 principal components have greater than one eigenvalue, the first 8 principal components have greater than 2 eigenvalue and the first 5 components have greater than 3.76 eigenvalue.
Principal components have been used as model inputs, when the variable space is too large and, specially, when models are particularly sensitive to the number of variables (e.g. Neural Networks) [78].
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Three principal components having eigenvalues greater than 1 were considered.
Principal components having Eigen values greater than 1 were extracted for this study.
The estimation of the model order, also known as the number of principal components, has been investigated in several science fields, and usually model order selection schemes are proposed only for specific scenarios in the literature.
The first seven principal components had an eigenvalue of more than 1.
The first 2 principal components had 78% of the total variation.
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