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 9 articles , 1 standard article )

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  1. 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)
  2. Ji, Zhicheng; Ji, Hongkai: Discussion of “Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data” (2021)
  3. 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)
  4. Lin, Kevin Z.; Lei, Jing; Roeder, Kathryn: Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data (2021)
  5. 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
  6. Schoen, Fabio; Tigli, Luca: Efficient large scale global optimization through clustering-based population methods (2021)
  7. Isotta Landi, Veronica Mandelli, Michael V. Lombardo: reval: a Python package to determine the best number of clusters with stability-based relative clustering validation (2020) arXiv
  8. Lavrač, Nada; Škrlj, Blaž; Robnik-Šikonja, Marko: Propositionalization and embeddings: two sides of the same coin (2020)
  9. Leland McInnes, John Healy, James Melville: UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction (2018) arXiv