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

TL;DR

Feature Clock offers a novel visualization method that simplifies understanding high-dimensional feature effects in 2D plots, improving interpretability without inspecting multiple plots.

Contribution

It introduces a new visualization technique that consolidates high-dimensional feature effects into a single, interpretable 2D visualization, enhancing explainability.

Findings

Reduces the need to inspect multiple plots for feature effects

Improves interpretability of high-dimensional data visualizations

Available as an open-source Python library

Abstract

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.