  Dimensionality Reduction (DR) techniques such as t-SNE and UMAP are popular
for transforming complex datasets into simpler visual representations. However,
while effective in uncovering general dataset patterns, these methods may
introduce artifacts and suffer from interpretability issues. This paper
presents DimVis, a visualization tool that employs supervised Explainable
Boosting Machine (EBM) models (trained on user-selected data of interest) as an
interpretation assistant for DR projections. Our tool facilitates
high-dimensional data analysis by providing an interpretation of feature
relevance in visual clusters through interactive exploration of UMAP
projections. Specifically, DimVis uses a contrastive EBM model that is trained
in real time to differentiate between the data inside and outside a cluster of
interest. Taking advantage of the inherent explainable nature of the EBM, we
then use this model to interpret the cluster itself via single and pairwise
feature comparisons in a ranking based on the EBM model's feature importance.
The applicability and effectiveness of DimVis are demonstrated via a use case
and a usage scenario with real-world data. We also discuss the limitations and
potential directions for future research.
