sominit.pca.dist: Initialise the prototypes of a dissimilarity SOM with...

In yasomi: Yet Another Self Organising Map Implementation

Description

Initialise the prototypes of a dissimilarity Self-Organising Map with a classical multidimensional scaling. The prototypes are regulary positioned (according to the prior structure) in the two dimensional space representation of the data according to a classical MDS. If the dissimilarity matrix contains squared Euclidean distances between vector data, then this method is equivalent to sominit.pca.default.

Usage

## S3 method for class 'dist'
sominit.pca(data, somgrid, weights, ...)

Arguments

data	the data to which the SOM will be fitted, i.e., a distance matrix of class "dist"
somgrid	a somgrid object
weights	optional weights for the data points
...	not used

Details

The method first conducts an eigen analysis of the doubly centred squared dissimilarity matrix. It generates a regular grid in the two dimensional representation space spanned by the first two eigen vectors and returns the coordinates of the nodes of the grid as linear combinations of the original points.

Value

A list with the following components

prototype	a matrix containing appropriate initial prototypes
D	the matrix of squared dissimilarities
D.c	the doubly centered matrix obtained from D
D.eigen	the result of the eigen analysis of D.c

Author(s)

Fabrice Rossi

See Also

sominit.random and somgrid

Examples

data(iris)

X <- scale(as.matrix(unique(iris[1:2])))

sg <- somgrid(xdim=7,ydim=7,topo="rect")

proto <- sominit.pca(dist(X),sg)$prototypes

proto.mapped <- proto%*%X

plot(X,pch="+",col="red",xlim=range(X[,1],proto.mapped[,1]),
     ylim=range(X[,2],proto.mapped[,2]))

points(proto.mapped,pch=20)

yasomi documentation built on May 2, 2019, 5:59 p.m.