Feature Clock: High-Dimensional Effects in Two-Dimensional Plots

Olga Ovcharenko, Rita Sevastjanova, Valentina Boeva

Abstract:

Humans struggle to perceive and interpret high-dimensional data.
Therefore, high-dimensional data are often projected into two di-
mensions 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.