A General Framework for Comparing Embedding Visualizations Across Class-Label Hierarchies.

ABSTRACT: Projecting high-dimensional vectors into two dimensions for visualization, known as embedding visualization, facilitates perceptual reasoning and interpretation. Comparing multiple embedding visualizations drives decision-making in many domains, but traditional comparison methods are limited by a reliance on direct point correspondences. This requirement precludes comparisons without point correspondences, such as two different datasets of annotated images, and fails to capture meaningful higher-level relationships among point groups. To address these shortcomings, we propose a general framework for comparing embedding visualizations based on shared class labels rather than individual points. Our approach partitions points into regions corresponding to three key class concepts-confusion, n

SUBMITTER: Manz T 

PROVIDER: S-EPMC11875997 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature