# HUMAP: Hierarchical Uniform Manifold Approximation and Projection

arXiv: https://arxiv.org/abs/2106.07718 · HTML: https://mlsanity.com/paper/2106.07718/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 59 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning](https://mlsanity.com/paper/1905.03911/) (2019)
- [Coarse Graining of Data via Inhomogeneous Diffusion Condensation](https://mlsanity.com/paper/1907.04463/) (2019)
- [Explaining dimensionality reduction results using Shapley values](https://mlsanity.com/paper/2103.05678/) (2021)
- [An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data](https://mlsanity.com/paper/1905.04000/) (2019)
- [Contrastive analysis for scatterplot-based representations of dimensionality reduction](https://mlsanity.com/paper/2101.12044/) (2021)
- [Visual Feature Fusion and its Application to Support Unsupervised Clustering Tasks](https://mlsanity.com/paper/1901.05556/) (2019)

## Built on by (most cited first, 4 of 4)

- [Semantic Communities and Boundary-Spanning Lyrics in K-pop: A Graph-Based Unsupervised Analysis](https://mlsanity.com/paper/2602.12881/) (2026)
- [Manifold-Preserving Superpixel Hierarchies and Embeddings for the Exploration of High-Dimensional Images](https://mlsanity.com/paper/2602.24160/) (2026)
- [Navigating the Concept Space of Language Models](https://mlsanity.com/paper/2603.23524/) (2026)
- [A Spectral Decomposition Framework for Multiscale Nonlinear Dimensionality Reduction](https://mlsanity.com/paper/2604.02535/) (2026)

## Abstract (arXiv)

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.