🤖 AI Summary
Existing hierarchical dimensionality reduction methods struggle to simultaneously preserve both local and global structures across multiple granularity levels while maintaining users’ mental map continuity. To address this, we propose HUMAP—the first hierarchical dimensionality reduction framework built upon UMAP. HUMAP achieves dual optimization of structural fidelity and mental map consistency through three core techniques: cross-scale neighborhood graph propagation, multi-granularity similarity modeling, and incremental embedding alignment. Compared to state-of-the-art methods, HUMAP significantly improves hierarchical structure preservation across multiple benchmark datasets. Furthermore, it demonstrates strong interpretability and interactive utility in real-world data labeling tasks. By unifying geometric structure preservation with cognitive consistency, HUMAP establishes a novel paradigm for multi-granularity visual analytics, enabling scalable, intuitive, and semantically grounded exploration of high-dimensional hierarchical data.
📝 Abstract
Dimensionality reduction (DR) techniques help analysts to 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. This work presents HUMAP, a novel hierarchical dimensionality reduction technique designed to be flexible on preserving local and global structures and preserve the mental map throughout hierarchical exploration. We provide empirical evidence of our technique’s superiority compared with current hierarchical approaches and show a case study applying HUMAP for dataset labelling.