Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

In this work, we propose an interactive visual approach for the exploration and formation of structural relationships in embeddings of high-dimensional data. These structural relationships, such as item sequences, associations of items with groups, and hierarchies between groups of items, are defining properties of many real-world datasets. Nevertheless, most existing methods for the visual exploration of embeddings treat these structures as second-class citizens or do not take them into account at all. In our proposed analysis workflow, users explore enriched scatterplots of the embedding, in which relationships between items and/or groups are visually highlighted. The original high-dimensional data for single items, groups of items, or differences between connected items and groups are accessible through additional summary visualizations. We carefully tailored these summary and difference visualizations to the various data types and semantic contexts. During their exploratory analysis, users can externalize their insights by setting up additional groups and relationships between items and/or groups. We demonstrate the utility and potential impact of our approach by means of two use cases and multiple examples from various domains.

Automated summary

In this paper, we propose an interactive visual approach for exploring and forming structural relationships in high-dimensional data embeddings. Most existing methods ignore or do not prioritize these structural relationships. Our approach allows users to visually explore enriched scatterplots of the embedding, highlighting relationships between items and/or groups. The original high-dimensional data and differences between connected items and groups can be accessed through additional summary visualizations. We demonstrate the utility and potential impact of our approach through two use cases and multiple examples from various domains.