addRowWiseC

Adds the Vector DataPoint to every row of the matrix WeightVectors

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>.

Michael Thrun

GeneralizedUmatrix

Credible Visualization for Two-Dimensional Projections of Data

Felix Pape

Tim Schreier

Luis Winckelman

Quirin Stier

Alfred Ultsch

addRowWiseC function

<dl><dt>WeightVectors</dt>
<dd>WeightVectors. n weights with m components each</dd><dt>DataPoint</dt>
<dd>Vector with m components</dd></dl>

Arguments

intern function — addRowWiseC

<dl>

<dt>WeightVectors</dt>
<dd>WeightVectors. n weights with m components each</dd>

<dt>DataPoint</dt>
<dd>Vector with m components</dd>

</dl>

addRowWiseC: intern function

Description

Usage

Value

Arguments