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>.
| Version: |
0.1.14 |
| Depends: |
R (≥ 3.0) |
| Imports: |
Rcpp (≥ 1.0.8), RcppParallel (≥ 5.1.4), ggplot2, GeneralizedUmatrix |
| LinkingTo: |
Rcpp, RcppArmadillo, RcppParallel |
| Suggests: |
DataVisualizations, rgl, grid, mgcv, png, reshape2, fields, ABCanalysis, plotly, deldir, methods, knitr (≥ 1.12), rmarkdown (≥ 0.9) |
| Published: |
2026-01-27 |
| DOI: |
10.32614/CRAN.package.GeneralizedUmatrixGPU |
| Author: |
Quirin Stier 
[aut, cre],
Michael Thrun
[aut, cph],
The Khronos Group Inc. [cph] |
| Maintainer: |
Quirin Stier <Quirin_Stier at gmx.de> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| SystemRequirements: |
C++17, GNU make, pandoc (>=1.12.3, needed for
vignettes), OpenCL shared library (provided by an SDK such as
AMD/NVIDIA) |
| Citation: |
GeneralizedUmatrixGPU citation info |
| CRAN checks: |
GeneralizedUmatrixGPU results |