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.

翻译：降维技术（如t-SNE和UMAP）因其将复杂数据集转化为更简洁视觉表示的能力而广受欢迎。然而，尽管这些方法能有效揭示数据集的总体模式，但也可能引入伪影并面临可解释性问题。本文提出DimVis，一种可视化工具，该工具采用有监督的可解释提升机模型（基于用户选定的感兴趣数据训练）作为降维投影的解释助手。我们的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性EBM模型，区分感兴趣聚类内部与外部数据。利用EBM固有的可解释特性，我们基于该模型的特征重要性排序，通过单特征与成对特征比较来解读聚类本身。通过实际数据用例及使用场景验证了DimVis的适用性和有效性，并讨论了其局限性及未来研究方向。