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However, Pearson's correlation is sensitive to outliers.
Because the correlation is sensitive to the SNR in the image data, the accuracy of the correlation measure can be improved by weighting the correlation of Fourier components according to their SNR.
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This is because the normalized cross-correlation is sensitive to rotation and scale changes.
These correlations are sensitive to the region and number of data points.
Apparently, such correlations are sensitive to the strength genetic drifts (N e ) in different organisms (genomes).
The Pearson's correlation coefficient is sensitive to different data distributions, requiring normal distribution and linear relationships between variables.
P(θ r ) is the distribution function reflecting the k-j ' correlation, which is sensitive to two factors: the characters of PES and the mass factor.
As shown in Figure 12, the correlation matrix is sensitive when the number of sample pixels is less than 10 times of the number of bands L(Indexes III and IV).
However, Pearson's correlation coefficient is sensitive to outliers [ 13].
f Since we use Spearman's rank correlation coefficient (which is sensitive to outliers), the variables were transformed as described above before computing the specific values.
Unlike correlation, however, it is sensitive to nonlinear interactions, works on general nonparametric inference, and naturally performs well on discrete data.
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