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:
this https URL

基于 Mapper 和 STAD 的灵感，本文针对 UMAP 提出了三种类型的镜头函数，用于领域知识引导的交互式探索，以实现将高维数据投影到二维空间并显示隐藏模式的目的。通过使用镜头函数，分析人员可以根据问题调整投影，揭示不可见的模式，并通过筛选连接性来探索手动选择的特征与数据结构之间的相互作用，从而创造可能揭示新见解的可配置视角。本文在两个使用案例中展示了镜头函数的有效性，并在合成基准测试中分析了它们的计算成本。