ICA | R Documentation |
Independent Component Analysis:
Negentropie: difference of entropy to a corresponding normally-distributed random variable J(y)=|E(G(y)-E(G(v)))|^2
ICA(Data,OutputDimension=2,Contrastfunction="logcosh", Alpha=1,Iterations=200,PlotIt=FALSE,Cls)
Data |
numerical matrix of n cases in rows, d variables in columns, matrix is not symmetric. |
OutputDimension |
Number of dimensions in the Outputspace, default=2 |
Contrastfunction |
Maximierung der Negentropie ueber geeignete geeignete Kontrastfunktion Default: 'logcosh' G(u)=1/a*log cosh(a*u) 'exp': G(u)=-exp(u^2/2) |
Alpha |
onstant 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. |
An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (doi: 10.1007/978-3-658-20540-9).
ProjectedPoints |
[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projectio |
Mixing |
[1:OutputDimension,1:d] Mischungsmatrix s.d gilt Data=MixingMatrix*ProjectedPoints |
Unmixing |
Entmischungsmatrix with Data*Unmixing=ProjectedPoints |
PCMatrix |
pre-whitening matrix that projects data onto the first n.comp principal components. |
A wrapper for fastICA
You can use the standard ShepardScatterPlot
or the better approach through the ShepardDensityPlot
of the CRAN package DataVisualizations
.
Michael Thrun
data('Hepta') Data=Hepta$Data Proj=ICA(Data) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.