ProjectionPursuit: Projection Pursuit

In ProjectionBasedClustering: Projection Based Clustering

Projection Pursuit

Description

In the absence of a generative model for the data the algorithm can be used to find the projection pursuit directions. Projection pursuit is a technique for finding 'interesting' directions in multidimensional datasets

Usage

ProjectionPursuit(Data,OutputDimension=2,Indexfunction="logcosh",

Alpha=1,Iterations=200,PlotIt=FALSE,Cls)

Arguments

Data	array of data: n cases in rows, d variables in columns, matrix is not symmetric or distance matrix, in this case matrix has to be symmetric
OutputDimension	Number of dimensions in the Outputspace, default=2
Indexfunction	Criterium for Minimization: default: 'logcosh' G(u)=1/a*log cosh(a*u) (ICA) 'exp': G(u)=-exp(u^2/2) 'kernel' 1/(1* pi )*exp(r/2)
Alpha	constant with 1<=alpha<=2 used in approximation to neg-entropy when fun == "logcosh"
Iterations	maximum number of iterations to perform.
PlotIt	Default: FALSE, If TRUE: Plots the projection as a 2d visualization. OutputDimension>2: only the first two dimensions will be shown
Cls	[1:n,1] Optional,: 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 Projectio

Note

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

Author(s)

Michael Thrun

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