  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.
