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 37 articles , 1 standard article )

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  1. Agarwala, Susama; Dees, Ben; Gearheart, Andrew; Lowman, Corey: Geometry and generalization: eigenvalues as predictors of where a network will fail to generalize (2022)
  2. Costa, Gianni; Ortale, Riccardo: Hierarchical Bayesian text modeling for the unsupervised joint analysis of latent topics and semantic clusters (2022)
  3. Escolano, Carlos; Costa-jussà, Marta R.; Fonollosa, José A. R.: Multilingual machine translation: deep analysis of language-specific encoder-decoders (2022)
  4. Fagni, Tiziano; Cresci, Stefano: Fine-grained prediction of political leaning on social media with unsupervised deep learning (2022)
  5. Fanuel, Michaël; Aspeel, Antoine; Delvenne, Jean-Charles; Suykens, Johan A. K.: Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions (2022)
  6. Frost, H. Robert: Eigenvectors from eigenvalues sparse principal component analysis (2022)
  7. Hu, C.; Martin, S.; Dingreville, R.: Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space (2022)
  8. Linderman, George C.; Steinerberger, Stefan: Dimensionality reduction via dynamical systems: the case of t-SNE (2022)
  9. Paradis, Emmanuel: Reduced multidimensional scaling (2022)
  10. Rudin, Cynthia; Chen, Chaofan; Chen, Zhi; Huang, Haiyang; Semenova, Lesia; Zhong, Chudi: Interpretable machine learning: fundamental principles and 10 grand challenges (2022)
  11. Škrlj, Blaž; Džeroski, Sašo; Lavrač, Nada; Petković, Matej: ReliefE: feature ranking in high-dimensional spaces via manifold embeddings (2022)
  12. Song, Anna: Generation of tubular and membranous shape textures with curvature functionals (2022)
  13. William E. Carson IV, Austin Talbot, David Carlson: AugmentedPCA: A Python Package of Supervised and Adversarial Linear Factor Models (2022) arXiv
  14. Bonasera, Stefano; Bosanac, Natasha: Applying data mining techniques to higher-dimensional Poincaré maps in the circular restricted three-body problem (2021)
  15. Boubekki, Ahcène; Kampffmeyer, Michael; Brefeld, Ulf; Jenssen, Robert: Joint optimization of an autoencoder for clustering and embedding (2021)
  16. 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)
  17. Ji, Zhicheng; Ji, Hongkai: Discussion of “Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data” (2021)
  18. 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
  19. Kadıoğlu, Serdar; Kleynhans, Bernard; Wang, Xin: Optimized item selection to boost exploration for recommender systems (2021)
  20. Kang, Bo; García García, Darío; Lijffijt, Jefrey; Santos-Rodríguez, Raúl; De Bie, Tijl: Conditional t-SNE: more informative t-SNE embeddings (2021)

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