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
ProjectionPursuit(Data,OutputDimension=2,Indexfunction="logcosh",Alpha=1,Iterations=200,PlotIt=FALSE,Cls)
[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projectio
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
Number of dimensions in the Outputspace, default=2
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)
constant with 1<=alpha<=2 used in approximation to neg-entropy when fun == "logcosh"
maximum number of iterations to perform.
Default: FALSE, If TRUE: Plots the projection as a 2d visualization. OutputDimension>2: only the first two dimensions will be shown
[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.
Michael Thrun
An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (tools:::Rd_expr_doi("10.1007/978-3-658-20540-9")).