Lens functions for exploring UMAP Projections with Domain Knowledge
This work addresses the need for more interpretable and customizable visualizations in data analysis, though it is incremental as it adapts existing techniques to UMAP.
The paper tackles the problem of enhancing interactive exploration of UMAP projections by introducing lens functions that incorporate domain knowledge, resulting in configurable perspectives that reveal hidden patterns, as demonstrated in two use cases with computational cost analysis.
Dimensionality reduction algorithms are often used to visualise high-dimensional data. Previously, studies have used prior information to enhance or suppress expected patterns in projections. In this paper, we adapt such techniques for domain knowledge guided interactive exploration. Inspired by Mapper and STAD, we present three types of lens functions for UMAP, a state-of-the-art dimensionality reduction algorithm. Lens functions enable analysts to adapt projections to their questions, revealing otherwise hidden patterns. They filter the modelled connectivity to explore the interaction between manually selected features and the data's structure, creating configurable perspectives each potentially revealing new insights. The effectiveness of the lens functions is demonstrated in two use cases and their computational cost is analysed in a synthetic benchmark. Our implementation is available in an open-source Python package: https://github.com/vda-lab/lensed_umap.