# Manifold-aligned Neighbor Embedding

arXiv: https://arxiv.org/abs/2205.11257 · HTML: https://mlsanity.com/paper/2205.11257/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 3 of 3)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)

## Built on by (most cited first, 7 of 7)

- [ANTM: An Aligned Neural Topic Model for Exploring Evolving Topics](https://mlsanity.com/paper/2302.01501/) (2023)
- [The Shape of Attraction in UMAP: Exploring the Embedding Forces in Dimensionality Reduction](https://mlsanity.com/paper/2503.09101/) (2025)
- [Outlier Detection in Large Radiological Datasets using UMAP](https://mlsanity.com/paper/2407.21263/) (2024)
- [Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias](https://mlsanity.com/paper/2506.16494/) (2025)
- [Lens functions for exploring UMAP Projections with Domain Knowledge](https://mlsanity.com/paper/2405.09204/) (2024)
- [Sobolev--Ricci Curvature](https://mlsanity.com/paper/2603.12652/) (2026)
- [On Out-of-sample Embedding in UMAP](https://mlsanity.com/paper/2606.04451/) (2026)

## Abstract (arXiv)

In this paper, we introduce a neighbor embedding framework for manifold alignment. We demonstrate the efficacy of the framework using a manifold-aligned version of the uniform manifold approximation and projection algorithm. We show that our algorithm can learn an aligned manifold that is visually competitive to embedding of the whole dataset.