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]. 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 based on the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) .


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install.packages("GeneralizedUmatrix")

1.2.0 by Michael Thrun, a month ago


http://www.deepbionics.org


Report a bug at https://github.com/Mthrun/GeneralizedUmatrix/issues


Browse source code at https://github.com/cran/GeneralizedUmatrix


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


Documentation:   PDF Manual  


GPL-3 license


Imports Rcpp, ggplot2

Suggests DataVisualizations, rgl, grid, mgcv, png, reshape2, fields, ABCanalysis, plotly, deldir, methods, knitr, rmarkdown

Linking to Rcpp, RcppArmadillo

System requirements: C++11


Imported by DatabionicSwarm, ProjectionBasedClustering.

Suggested by FCPS.


See at CRAN