Credible Visualization for Two-Dimensional Projections of Data
intern function
Calculate the U*matrix for a given Umatrix and Pmatrix.
intern function
Converts wts data (EsomNeurons) into the list form
Extend Toroidal Umatrix
tools:::Rd_package_title("GeneralizedUmatrix")
Generalized U-Matrix for Projection Methods published in [Thrun/Ultsch...
Generates the P-matrix
Converts List to WTS
LowLand
Normalize Umatrix
Visualizes the generalized U-matrix in 3D
ReduceToLowLand
simplified ESOM
setdiffMatrix shortens Matrix2Curt by those rows that are in both matr...
Top view of the topographic map in 2D
internal function for s-esom
internal function for s-esom
Uheights4Data
UniqueBestMatchingUnits
Upscale a Umatrix grid
XYcoords2LinesColumns(X,Y) Converts points given as x(i),y(i) coordina...
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>.