Independent Component Analysis
Negentropie: Entropiedifferenz zu einer entsprechenden normalverteilten Zufallsvariable J(y)=|E(G(y)-E(G(v)))|^2
ICA(Data,OutputDimension=2,Contrastfunction="logcosh",Alpha=1,Iterations=200,PlotIt=FALSE,Cls)
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
Maximierung der Negentropie ueber geeignete geeignete Kontrastfunktion Default: 'logcosh' G(u)=1/a*log cosh(a*u) 'exp': G(u)=-exp(u^2/2)
onstant 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.
[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projectio
[1:OutputDimension,1:d] Mischungsmatrix s.d gilt Data=MixingMatrix*ProjectedPoints
Entmischungsmatrix mit Data*Unmixing=ProjectedPoints
pre-whitening matrix that projects data onto the first n.comp principal components.