esomTrain: Train an ESOM (emergent self organizing map) and project data

In Umatrix: Visualization of Structures in High-Dimensional Data

Train an ESOM (emergent self organizing map) and project data

Description

The ESOM (emergent self organizing map) algorithm as defined by [Ultsch 1999]. A set of weigths(neurons) on a two-dimensional grid get trained to adapt the given datastructure. The weights will be used to project data on a two-dimensional space, by seeking the BestMatches for every datapoint.

Arguments

Data	Data that will be used for training and projection
Lines	Height of grid
Columns	Width of grid
Epochs	Number of Epochs the ESOM will run
Toroid	If TRUE, the grid will be toroid
NeighbourhoodFunction	Type of Neighbourhood; Possible values are: "cone", "mexicanhat" and "gauss"
StartLearningRate	Initial value for LearningRate
EndLearningRate	Final value for LearningRate
StartRadius	Start value for the Radius in which will be searched for neighbours
EndRadius	End value for the Radius in which will be searched for neighbours
NeighbourhoodCooling	Cooling method for radius; "linear" is the only available option at the moment
LearningRateCooling	Cooling method for LearningRate; "linear" is the only available option at the moment
shinyProgress	Generate progress output for shiny if Progress Object is given
ShiftToHighestDensity	If True, the Umatrix will be shifted so that the point with highest density will be at the center
InitMethod	name of the method that will be used to choose initializations Valid Inputs: "uni_min_max": uniform distribution with minimum and maximum from sampleData "norm_mean_std": normal distribuation based on mean and standard deviation of sampleData
Key	Vector of numeric keys matching the datapoints. Will be added to Bestmatches
UmatrixForEsom	If TRUE, Umatrix based on resulting ESOM is calculated and returned

Details

On a toroid grid, opposing borders are connected.

Value

List with

BestMatches	BestMatches of datapoints
Weights	Trained weights
Lines	Height of grid
Columns	Width of grid
Toroid	TRUE if grid is a toroid
JumpingDataPointsHist	Nr of DataPoints that jumped to a different BestMatch in every epoch

References

Kohonen, T., Self-organized formation of topologically correct feature maps. Biological cybernetics, 1982. 43(1): p. 59-69.

Ultsch, A., Data mining and knowledge discovery with emergent self-organizing feature maps for multivariate time series. Kohonen maps, 1999. 46: p. 33-46.

Examples

data('Hepta')
res=esomTrain(Hepta$Data, Key = 1:nrow(Hepta$Data))

Umatrix documentation built on Sept. 11, 2024, 8:09 p.m.