UMAP

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Furthermore, UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning.


References in zbMATH (referenced in 26 articles , 1 standard article )

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  1. Fanuel, Michaël; Aspeel, Antoine; Delvenne, Jean-Charles; Suykens, Johan A. K.: Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions (2022)
  2. Linderman, George C.; Steinerberger, Stefan: Dimensionality reduction via dynamical systems: the case of t-SNE (2022)
  3. Rudin, Cynthia; Chen, Chaofan; Chen, Zhi; Huang, Haiyang; Semenova, Lesia; Zhong, Chudi: Interpretable machine learning: fundamental principles and 10 grand challenges (2022)
  4. William E. Carson IV, Austin Talbot, David Carlson: AugmentedPCA: A Python Package of Supervised and Adversarial Linear Factor Models (2022) arXiv
  5. Bonasera, Stefano; Bosanac, Natasha: Applying data mining techniques to higher-dimensional Poincaré maps in the circular restricted three-body problem (2021)
  6. Boubekki, Ahcène; Kampffmeyer, Michael; Brefeld, Ulf; Jenssen, Robert: Joint optimization of an autoencoder for clustering and embedding (2021)
  7. Coimbra, Danilo B.; Martins, Rafael M.; Mota, Edson; Tiburtino, Tacito; Diamantino, Pedro; Peixoto, Maycon L. M.: Analyzing the quality of local and global multidimensional projections using performance evaluation planning (2021)
  8. Ji, Zhicheng; Ji, Hongkai: Discussion of “Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data” (2021)
  9. Joseph Paul Cohen, Joseph D. Viviano, Paul Bertin, Paul Morrison, Parsa Torabian, Matteo Guarrera, Matthew P Lungren, Akshay Chaudhari, Rupert Brooks, Mohammad Hashir, Hadrien Bertrand: TorchXRayVision: A library of chest X-ray datasets and models (2021) arXiv
  10. Kadıoğlu, Serdar; Kleynhans, Bernard; Wang, Xin: Optimized item selection to boost exploration for recommender systems (2021)
  11. Kang, Bo; García García, Darío; Lijffijt, Jefrey; Santos-Rodríguez, Raúl; De Bie, Tijl: Conditional t-SNE: more informative t-SNE embeddings (2021)
  12. Kileel, Joe; Moscovich, Amit; Zelesko, Nathan; Singer, Amit: Manifold learning with arbitrary norms (2021)
  13. Lasri, Ayoub; Sturrock, Marc: The influence of methylation status on a stochastic model of MGMT dynamics in glioblastoma: phenotypic selection can occur with and without a downshift in promoter methylation status (2021)
  14. Lin, Kevin Z.; Lei, Jing; Roeder, Kathryn: Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data (2021)
  15. Luo, Hengrui; Patania, Alice; Kim, Jisu; Vejdemo-Johansson, Mikael: Generalized penalty for circular coordinate representation (2021)
  16. Mizuno, Yuta; Takigawa, Mikoto; Miyashita, Saki; Nagahata, Yutaka; Teramoto, Hiroshi; Komatsuzaki, Tamiki: An algorithm for computing phase space structures in chemical reaction dynamics using Voronoi tessellation (2021)
  17. Mulas, Raffaella; Casey, Michael J.: Estimating cellular redundancy in networks of genetic expression (2021)
  18. Philippe Boileau, Nima Hejazi, Brian Collica, Jamarcus Liu, Mark van der Laan, Sandrine Dudoit : cvCovEst: Cross-validated covariance matrix estimator selection and evaluation in R (2021) not zbMATH
  19. Sainburg, Tim; Mcinnes, Leland; Gentner, Timothy Q.: Parametric UMAP embeddings for representation and semisupervised learning (2021)
  20. Schoen, Fabio; Tigli, Luca: Efficient large scale global optimization through clustering-based population methods (2021)

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