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GeneralizedUmatrixGPU  

Credible Visualization for Two-Dimensional Projections of Data
View on CRAN: Click here


Download and install GeneralizedUmatrixGPU package within the R console
Install from CRAN:
install.packages("GeneralizedUmatrixGPU")

Install from Github:
library("remotes")
install_github("cran/GeneralizedUmatrixGPU")

Install by package version:
library("remotes")
install_version("GeneralizedUmatrixGPU", "0.1.14")



Attach the package and use:
library("GeneralizedUmatrixGPU")
Maintained by
Quirin Stier
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-11-17
Latest Update: 2025-11-17
Description:
Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] <doi:10.1007/978-3-658-20540-9>. This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <doi:10.1007/978-3-658-20540-9> and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in Thrun, M.C. and Ultsch, A.: "Uncovering High-dimensional Structures of Projections from Dimensionality Reduction Methods" (2020) <doi:10.1016/j.mex.2020.101093>.
How to cite:
Quirin Stier (2025). GeneralizedUmatrixGPU: Credible Visualization for Two-Dimensional Projections of Data. R package version 0.1.14, https://cran.r-project.org/web/packages/GeneralizedUmatrixGPU. Accessed 24 Oct. 2026.
Previous versions and publish date:
0.1.8 (2025-11-17 21:50), 0.1.10 (2026-01-14 09:00), (2026-07-09 08:05)
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