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

Published: 01 Dec 2024, Last Modified: 28 May 2025IEEE VIS 2024EveryoneCC BY 4.0
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.
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