  In high-dimensional data analysis the curse of dimensionality reasons that
points tend to be far away from the center of the distribution and on the edge
of high-dimensional space. Contrary to this, is that projected data tends to
clump at the center. This gives a sense that any structure near the center of
the projection is obscured, whether this is true or not. A transformation to
reverse the curse, is defined in this paper, which uses radial transformations
on the projected data. It is integrated seamlessly into the grand tour
algorithm, and we have called it a burning sage tour, to indicate that it
reverses the curse. The work is implemented into the tourr package in R.
Several case studies are included that show how the sage visualizations enhance
exploratory clustering and classification problems.

Computation