Isomap: Isomap

In ProjectionBasedClustering: Projection Based Clustering

Isomap

Description

Isomap procetion as introduced in 2000 by Tenenbaum, de Silva and Langford

Even with a manifold structure, the sampling must be even and dense so that dissimilarities along a manifold are shorter than across the folds. If data do not have such a manifold structure, the results are very sensitive to parameter values.

Usage

Isomap(Distances,k,OutputDimension=2,PlotIt=FALSE,Cls)

Arguments

Distances	Symmetric [1:n,1:n] distance matrix, e.g. as.matrix(dist(Data,method))
k	number of k nearest neighbors, if the data is fragmented choose an higher k
OutputDimension	Number of dimensions in the output space, default = 2
PlotIt	Default: FALSE, If TRUE: Plots the projection as a 2d visualization. If OutputDimension > 2 only the first two dimensions will be shown.
Cls	Optional and only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data.

Details

An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-3-658-20540-9")}).

Value

ProjectedPoints[1:n,OutputDimension] n by OutputDimension matrix containing coordinates of the Projection: A matrix of the fitted configuration..

Note

A wrapper enabling a planar projection of the manifold learning method based on the isomap of the package vegan

if Data fragmented choose an higher k

You can use the standard ShepardScatterPlot or the better approach through the ShepardDensityPlot of the CRAN package DataVisualizations.

Author(s)

Michael Thrun

Examples

data('Hepta')
Data=Hepta$Data

Proj=Isomap(as.matrix(dist(Data)),k=7)

## Not run: 
PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls)

## End(Not run)

ProjectionBasedClustering documentation built on Oct. 12, 2023, 1:07 a.m.