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Even though the easiest way to detect the multivariate outliers is multidimensional scatter plot, some methods based on the Mahalanobis distance or Cooks distance have been suggested in the literature.
First, the predictor space (i.e. the multidimensional scatter plot of the predictors) is mapped into a high dimensional 'feature space'.
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Figure 1 presents the two-dimensional multidimensional scaling [21] scatter plots of the fractional edge weight distributions (blue circles) for our example networks and the disparity filter's distribution (red star), according to this pairwise distance.
Above: scatter plot between diff.
Figure 9 Scatter plot VQM for LIVE Wireless.
Notes scatter plot and quadratic prediction curve.
(a) Scatter plot of GBIM (b) scatter plot of Wang's blocking metric (c) scatter plot of Jeong's blocking metric (d) scatter plot of the proposed blocking metric.
Fig. 1 Scatter plot representing DER/AD.
Figure 5 Scatter plot and fitted contours.
Above: scatter plot between DMOS and PSNR.
(a) Scatter plot of Marziliano's blur metric (b) scatter plot of JNBM (c) scatter plot of Jeong's blur metric (d) scatter plot of the proposed blur metric.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com