  Dimensionality reduction (DR) techniques help analysts understand patterns in
high-dimensional spaces. These techniques, often represented by scatter plots,
are employed in diverse science domains and facilitate similarity analysis
among clusters and data samples. For datasets containing many granularities or
when analysis follows the information visualization mantra, hierarchical DR
techniques are the most suitable approach since they present major structures
beforehand and details on demand. However, current hierarchical DR techniques
are not fully capable of addressing literature problems because they do not
preserve the projection mental map across hierarchical levels or are not
suitable for most data types. This work presents HUMAP, a novel hierarchical
dimensionality reduction technique designed to be flexible in preserving local
and global structures and the mental map throughout hierarchical exploration.
We provide empirical evidence of our technique's superiority compared with
current hierarchical approaches and show two case studies to demonstrate its
strengths.
