Package: GeneralizedUmatrix 1.2.6

GeneralizedUmatrix: Credible Visualization for Two-Dimensional Projections of Data

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>.

Authors:Michael Thrun [aut, cre, cph], Felix Pape [ctb, ctr], Tim Schreier [ctb, ctr], Luis Winckelman [ctb, ctr], Quirin Stier [ctb, ctr], Alfred Ultsch [ths]

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# Install 'GeneralizedUmatrix' in R:
install.packages('GeneralizedUmatrix', repos = c('https://mthrun.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mthrun/generalizedumatrix/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

5.97 score 1 stars 6 packages 52 scripts 620 downloads 15 exports 31 dependencies

Last updated 1 years agofrom:6bb9b43fe1. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 07 2024
R-4.5-win-x86_64NOTENov 07 2024
R-4.5-linux-x86_64NOTENov 07 2024
R-4.4-win-x86_64NOTENov 07 2024
R-4.4-mac-x86_64NOTENov 07 2024
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R-4.3-win-x86_64NOTENov 07 2024
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Exports:CalcUstarmatrixEsomNeuronsAsListExtendToroidalUmatrixGeneralizedUmatrixGeneratePmatrixListAsEsomNeuronsLowLandNormalizeUmatrixplotTopographicMapReduceToLowLandTopviewTopographicMapUheights4DataUniqueBestMatchingUnitsupscaleUmatrixXYcoords2LinesColumns

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppParallelrlangscalestibbleutf8vctrsviridisLitewithr

Uncovering High-Dimensional Structures of Projections from Dimensionality Reduction Methods

Rendered fromGeneralizedUmatrix.Rmdusingknitr::rmarkdownon Nov 07 2024.

Last update: 2021-01-13
Started: 2019-01-29