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 through two use cases involving real-world datasets, and we also discuss the limitations and potential directions for future research.

翻译：降维技术（如t-SNE和UMAP）广泛用于将复杂数据集转化为更简洁的可视化表示。然而，尽管这些方法能有效揭示数据集整体模式，但可能引入伪影并存在可解释性问题。本文提出DimVis，一种利用监督式可解释提升机模型（基于用户选定感兴趣数据训练）作为降维投影解释辅助工具的可视化系统。该工具通过交互式探索UMAP投影，提供视觉聚类中特征相关性的解释，从而促进高维数据分析。具体而言，DimVis使用实时训练的对比式EBM模型，用于区分感兴趣聚类内外的数据。借助EBM固有的可解释特性，我们基于该模型特征重要性的排序，通过单特征与双特征比较来解析聚类本身。通过两个真实数据集用例展示DimVis的适用性与有效性，并讨论了当前局限性及未来研究方向。