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 <doi:10.1016/j.mex.2020.101093>.
Version: 1.3.1 Depends: R (≥ 3.0) Imports: Rcpp (≥ 1.0.8), RcppParallel (≥ 5.1.4), ggplot2 LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: DataVisualizations, rgl, grid, mgcv, png, reshape2, fields, ABCanalysis, plotly, deldir, methods, knitr (≥ 1.12), rmarkdown (≥ 0.9) Published: 2025-01-29 DOI: 10.32614/CRAN.package.GeneralizedUmatrix Author: Michael Thrun [aut, cre, cph], Felix Pape [ctb, ctr], Tim Schreier [ctb, ctr], Luis Winckelman [ctb, ctr], Quirin Stier [ctb, ctr], Alfred Ultsch [ths] Maintainer: Michael Thrun <m.thrun at gmx.net> BugReports: https://github.com/Mthrun/GeneralizedUmatrix/issues License: GPL-3 URL: https://www.deepbionics.org NeedsCompilation: yes SystemRequirements: GNU make, pandoc (>=1.12.3, needed for vignettes) Citation: GeneralizedUmatrix citation info Materials: README CRAN checks: GeneralizedUmatrix results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=GeneralizedUmatrix to link to this page.
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