Lens functions for exploring UMAP Projections with Domain Knowledge

TL;DR

This paper introduces three lens functions for UMAP that incorporate domain knowledge, enabling interactive exploration of high-dimensional data and revealing hidden patterns through configurable projections.

Contribution

It adapts techniques inspired by Mapper and STAD to create lens functions for UMAP, facilitating domain knowledge-guided interactive data exploration.

Findings

Lens functions enable tailored projections revealing hidden data patterns.

Demonstrated effectiveness in two real-world use cases.

Analyzed computational cost with a synthetic benchmark.

Abstract

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.