trainstepC2

Does the training for fixed bestmatches in one epoch of the sESOM.

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

trainstepC2 function

<dl><dt>esomwts</dt>
<dd>array [1:Lines,1:Columns,1:Weights], WeightVectors that will be trained, internally transformed von NumericVector to cube</dd><dt>aux</dt>
<dd>array [1:Lines,1:Columns,1:2], meshgrid for output distance computation</dd><dt>DataSampled</dt>
<dd>NumericMatrix, n cases shuffled Dataset[1:n,1:d] by <code>sample</code></dd><dt>BMUsampled</dt>
<dd>NumericMatrix, n cases shuffled BestMatches[1:n,1:2] by <code>sample</code> in the same way as <code>DataSampled</code></dd><dt>Lines</dt>
<dd>double, Height of the grid</dd><dt>Columns</dt>
<dd>double, Width of the grid</dd><dt>Weights</dt>
<dd>double, number of weights</dd><dt>Radius</dt>
<dd>double, The current Radius that should be used to define neighbours to the bm</dd><dt>toroid</dt>
<dd>bool, Should the grid be considered with cyclically connected borders?</dd><dt>NoCases</dt>
<dd>int, number of samples in the given non-sampled dataset</dd></dl>

Arguments

Author

internal function for s-esom — trainstepC2

<dl>

<dt>esomwts</dt>
<dd>array [1:Lines,1:Columns,1:Weights], WeightVectors that will be trained, internally transformed von NumericVector to cube</dd>

<dt>aux</dt>
<dd>array [1:Lines,1:Columns,1:2], meshgrid for output distance computation</dd>

<dt>DataSampled</dt>
<dd>NumericMatrix, n cases shuffled Dataset[1:n,1:d] by <code>sample</code></dd>

<dt>BMUsampled</dt>
<dd>NumericMatrix, n cases shuffled BestMatches[1:n,1:2] by <code>sample</code> in the same way as <code>DataSampled</code></dd>

<dt>Lines</dt>
<dd>double, Height of the grid</dd>

<dt>Columns</dt>
<dd>double, Width of the grid</dd>

<dt>Weights</dt>
<dd>double, number of weights</dd>

<dt>Radius</dt>
<dd>double, The current Radius that should be used to define neighbours to the bm</dd>

<dt>toroid</dt>
<dd>bool, Should the grid be considered with cyclically connected borders?</dd>

<dt>NoCases</dt>
<dd>int, number of samples in the given non-sampled dataset</dd>

</dl>

trainstepC2: internal function for s-esom

Description

Usage

Value

Arguments

Author

Details

References