mlsanityData to 14 Oct 2026

TriMap: Large-scale Dimensionality Reduction Using Triplets

arXiv 1910.00204 ↗First posted 1 Oct 2019cs.LG stat.MLcited by 60

Not on the map. Why?

Abstract (arXiv)

We introduce "TriMap"; a dimensionality reduction technique based on triplet constraints, which preserves the global structure of the data better than the other commonly used methods such as t-SNE, LargeVis, and UMAP. To quantify the global accuracy of the embedding, we introduce a score that roughly reflects the relative placement of the clusters rather than the individual points. We empirically show the excellent performance of TriMap on a large variety of datasets in terms of the quality of the embedding as well as the runtime. On our performance benchmarks, TriMap easily scales to millions of points without depleting the memory and clearly outperforms t-SNE, LargeVis, and UMAP in terms of runtime.

Key facts. Cited by 60 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Oct 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 8; 2023: 9; 2024: 10; 2025: 13; 2026: 14.

60 papers.

Sort:

By area:

  1. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 367
  2. 2021Where is your place, Visual Place Recognition?cited by 82
  3. 2021On UMAP's true loss functioncited by 32
  4. 2022From t-SNE to UMAP with contrastive learningcited by 28
  5. 2025Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Modelscited by 25
  6. 2022Unsupervised visualization of image datasets using contrastive learningcited by 16
  7. 2022Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reductioncited by 14
  8. 2022Learning from Randomly Initialized Neural Network Featurescited by 12
  9. 2021TLDR: Twin Learning for Dimensionality Reductioncited by 9
  10. 2025Stop Misusing t-SNE and UMAP for Visual Analyticscited by 8
  11. 2022InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Imagescited by 5
  12. 2025The Shape of Attraction in UMAP: Exploring the Embedding Forces in Dimensionality Reductioncited by 5
38 more
  1. 2021WiCluster: Passive Indoor 2D/3D Positioning using WiFi without Precise Labelscited by 4
  2. 2025Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality Reductioncited by 4
  3. 2025DREAMS: Preserving both Local and Global Structure in Dimensionality Reductioncited by 4
  4. 2022Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projectioncited by 3
  5. 2023Relating tSNE and UMAP to Classical Dimensionality Reductioncited by 3
  6. 2024Sampling-enabled scalable manifold learning unveils the discriminative cluster structure of high-dimensional datacited by 3
  7. 2024Curvature Augmented Manifold Embedding and Learningcited by 3
  8. 2024Outlier Detection in Large Radiological Datasets using UMAPcited by 3
  9. 2024Navigating the Effect of Parametrization for Dimensionality Reductioncited by 3
  10. 2025t-SNE Exaggerates Clusters, Provablycited by 3
  11. 2026Hodoscope: Unsupervised Monitoring for AI Misbehaviorscited by 3
  12. 2023Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasetscited by 2
  13. 2023k* Distribution: Evaluating the Latent Space of Deep Neural Networks using Local Neighborhood Analysiscited by 2
  14. 2025Metric Design != Metric Behavior: Improving Metric Selection for the Unbiased Evaluation of Dimensionality Reductioncited by 2
  15. 2022Layerwise Bregman Representation Learning with Applications to Knowledge Distillationcited by 1
  16. 2023Calibrating dimension reduction hyperparameters in the presence of noisecited by 1
  17. 2024Inference and Visualization of Community Structure in Attributed Hypergraphs Using Mixed-Membership Stochastic Block Modelscited by 1
  18. 2024MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learningcited by 1
  19. 2025UMATO: Bridging Local and Global Structures for Reliable Visual Analytics with Dimensionality Reductioncited by 1
  20. 2026FastUMAP: Scalable Dimensionality Reduction via Bipartite Landmark Samplingcited by 1
  21. 2026FloDR: An invertible dimensionality reduction method based on a normalising flowcited by 1
  22. 2021Optimality of the Johnson-Lindenstrauss Dimensionality Reduction for Practical Measurescited by 0
  23. 2022Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOMcited by 0
  24. 2023GroupEnc: encoder with group loss for global structure preservationcited by 0
  25. 2023Supervised Stochastic Neighbor Embedding Using Contrastive Learningcited by 0
  26. 2023Cluster Exploration using Informative Manifold Projectionscited by 0
  27. 2023Efficiently Visualizing Large Graphscited by 0
  28. 2023A ripple in time: a discontinuity in American historycited by 0
  29. 2024CBMAP: Clustering-based manifold approximation and projection for dimensionality reductioncited by 0
  30. 2024Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensionscited by 0
  31. 2024Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learningcited by 0
  32. 2024Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimationcited by 0
  33. 2025IIKL: Isometric Immersion Kernel Learning with Riemannian Manifold for Geometric Preservationcited by 0
  34. 2025Fast Geometric Embedding for Node Influence Maximizationcited by 0
  35. 2025Uncovering Latent Connections in Indigenous Heritage: Semantic Pipelines for Cultural Preservation in Brazilcited by 0
  36. 2025Cluster and then Embed: A Modular Approach for Visualizationcited by 0
  37. 2025Probabilistic Foundations of Fuzzy Simplicial Sets for Nonlinear Dimensionality Reductioncited by 0
  38. 2026Understanding and Improving UMAP with Geometric and Topological Priors: The JORC-UMAP Algorithmcited by 0

50 most cited of 60 All 60 (JSON)