  Two-dimensional embeddings obtained from dimensionality reduction techniques,
such as MDS, t-SNE, and UMAP, are widely used across various disciplines to
visualize high-dimensional data. These visualizations provide a valuable tool
for exploratory data analysis, allowing researchers to visually identify
clusters, outliers, and other interesting patterns in the data. However,
interpreting the resulting visualizations can be challenging, as it often
requires additional manual inspection to understand the differences between
data points in different regions of the embedding space. To address this issue,
we propose Visual Explanations via Region Annotation (VERA), an automatic
embedding-annotation approach that generates visual explanations for any
two-dimensional embedding. VERA produces informative explanations that
characterize distinct regions in the embedding space, allowing users to gain an
overview of the embedding landscape at a glance. Unlike most existing
approaches, which typically require some degree of manual user intervention,
VERA produces static explanations, automatically identifying and selecting the
most informative visual explanations to show to the user. We illustrate the
usage of VERA on a real-world data set and validate the utility of our approach
with a comparative user study. Our results demonstrate that the explanations
generated by VERA are as useful as fully-fledged interactive tools on typical
exploratory data analysis tasks but require significantly less time and effort
from the user.
