mlsanityData to 7 Oct 2026

Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction

arXiv 2203.12997 ↗First posted 24 Mar 2022cs.CV cs.AI cs.DScited by 14

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Abstract (arXiv)

Dimensionality reduction is crucial both for visualization and preprocessing high dimensional data for machine learning. We introduce a novel method based on a hierarchy built on 1-nearest neighbor graphs in the original space which is used to preserve the grouping properties of the data distribution on multiple levels. The core of the proposal is an optimization-free projection that is competitive with the latest versions of t-SNE and UMAP in performance and visualization quality while being an order of magnitude faster in run-time. Furthermore, its interpretable mechanics, the ability to project new data, and the natural separation of data clusters in visualizations make it a general purpose unsupervised dimension reduction technique. In the paper, we argue about the soundness of the proposed method and evaluate it on a diverse collection of datasets with sizes varying from 1K to 11M samples and dimensions from 28 to 16K. We perform comparisons with other state-of-the-art methods on multiple metrics and target dimensions highlighting its efficiency and performance. Code is available at https://github.com/koulakis/h-nne

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 11 of its 40 references that are in the map. First posted on arXiv 24 Mar 2022.

Builds on

11 of 40 references.

References · 11

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  4. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 853
  5. 2020Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualizationcited by 170
  6. 2020Parametric UMAP embeddings for representation and semi-supervised learningcited by 103
  7. 2019Efficient Parameter-free Clustering Using First Neighbor Relationscited by 96
  8. 2016Visualizing Large-scale and High-dimensional Datacited by 79
  9. 2017Efficient Algorithms for t-distributed Stochastic Neighborhood Embeddingcited by 75
  10. 2019TriMap: Large-scale Dimensionality Reduction Using Tripletscited by 59
  11. 2022Empirical complexity of comparator-based nearest neighbor descentcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 5; 2025: 2; 2026: 2.

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  1. 2022Panoramic Panoptic Segmentation: Insights Into Surrounding Parsing for Mobile Agents via Unsupervised Contrastive Learningcited by 14
  2. 2024Dimension Reduction with Locally Adjusted Graphscited by 9
  3. 2023Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentationcited by 7
  4. 2023DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentationcited by 7
  5. 2022Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projectioncited by 3
  6. 2024Curvature Augmented Manifold Embedding and Learningcited by 3
  7. 2024Task2Box: Box Embeddings for Modeling Asymmetric Task Relationshipscited by 3
  8. 2024MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualizationcited by 3
  9. 2024Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activationscited by 3
  10. 2023A survey of manifold learning and its applications for multimediacited by 1
  11. 2025OmniFall: From Staged Through Synthetic to Wild, A Unified Multi-Domain Dataset for Robust Fall Detectioncited by 1
  12. 2025LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point Cloud Active Learningcited by 0
2 more
  1. 2026The Rashomon Effect for Visualizing High-Dimensional Datacited by 0
  2. 2026CosMAP: Contrastive Manifold Approximation and Projection for Dimensionality Reduction of Omics and Genealogical Datacited by 0