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In this case, the maximum variance criterion should be used.
However, the maximum variance would be as large as 78.21, which is noticeably larger than the maximum variance from simple kriging.
To do so, PCA considers the maximum variance in the dataset, whereas MAD considers maximum autocorrelation, since it takes into account the maximum variance of the difference images.
It minimizes the maximum variance of the predicted value of the regression model.
The maximum variance criterion discussed above is used by Minasny et al. (2007).
The solution with the maximum variance reduction is the optimal solution.
The first principal component is associated with the axis that captures the maximum variance.
This is the maximum variance of the initial bi-variate data set.
Finally, the maximum variance of a given temporal profile is given by: (13).
The "maximum variance" method and the Minimum Circumscribed Circle/Ellipse formulations defined in the frequency domain are also discussed.
It ensures that the first principal component is proportional to the maximum variance of the input data.
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