  Humans struggle to perceive and interpret high-dimensional data. Therefore,
high-dimensional data are often projected into two dimensions for
visualization. Many applications benefit from complex nonlinear dimensionality
reduction techniques, but the effects of individual high-dimensional features
are hard to explain in the two-dimensional space. Most visualization solutions
use multiple two-dimensional plots, each showing the effect of one
high-dimensional feature in two dimensions; this approach creates a need for a
visual inspection of k plots for a k-dimensional input space. Our solution,
Feature Clock, provides a novel approach that eliminates the need to inspect
these k plots to grasp the influence of original features on the data structure
depicted in two dimensions. Feature Clock enhances the explainability and
compactness of visualizations of embedded data and is available in an
open-source Python library.
