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>.
||R (≥ 3.0)
||DataVisualizations, rgl, grid, mgcv, png, reshape2, fields, ABCanalysis, plotly, deldir, methods, knitr (≥ 1.12), rmarkdown (≥ 0.9)
[aut, cre, cph],
Felix Pape [ctb, ctr],
Tim Schreier [ctb, ctr],
Luis Winckelman [ctb, ctr],
Alfred Ultsch [ths]
||Michael Thrun <m.thrun at gmx.net>
||C++11, pandoc (>=1.12.3, needed for vignettes)
||GeneralizedUmatrix citation info
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