A Study on a User-Controlled Radial Tour for Variable Importance in High-Dimensional Data
Nicholas Spyrison, Dianne Cook, Kim Marriott

TL;DR
This study evaluates a user-controlled radial tour for visualizing variable importance in high-dimensional data, demonstrating it improves accuracy and user preference over PCA and grand tour methods.
Contribution
It introduces and empirically tests a novel user-controlled radial tour technique for assessing variable importance in high-dimensional data visualization.
Findings
Radial tour significantly increases accuracy in variable importance tasks.
Participants prefer the radial tour over PCA and grand tour.
Radial tour enhances interpretability of high-dimensional data projections.
Abstract
Principal component analysis is a long-standing go-to method for exploring multivariate data. The principal components are linear combinations of the original variables, ordered by descending variance. The first few components typically provide a good visual summary of the data. Tours also make linear projections of the original variables but offer many different views, like examining the data from different directions. The grand tour shows a smooth sequence of projections as an animation following interpolations between random target bases. The manual radial tour rotates the selected variable's contribution into and out of a projection. This allows the importance of the variable to structure in the projection to be assessed. This work describes a mixed-design user study evaluating the radial tour's efficacy compared with principal component analysis and the grand tour. A supervised…
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Taxonomy
TopicsData Visualization and Analytics · Sensory Analysis and Statistical Methods · Color perception and design
