DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine

TL;DR

DimVis is a visualization tool that uses explainable boosting machines to interpret and analyze visual clusters in dimensionality reduction projections, enhancing understanding of high-dimensional data.

Contribution

The paper introduces DimVis, a novel interactive visualization tool that employs supervised EBM models for real-time interpretation of clusters in DR visualizations.

Findings

Effective interpretation of clusters via feature relevance.

Real-time contrastive EBM models differentiate cluster data.

Enhanced understanding of high-dimensional data structures.

Abstract

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.