CCA Projects data vectors using Curvilinear Component Analysis.
Unknown values (NaN's) in the data: projections of vectors with unknown components tend to drift towards the center of the projection distribution. Projections of totally unknown vectors are set to unknown (NaN).
CCA(DataOrDists,Epochs,OutputDimension=2,method='euclidean',alpha0 = 0.5, lambda0,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 epochs (scalar) training length
(scalar) training length
Number of dimensions in the Outputspace, default=2
method specified by distance string. One of: 'euclidean','cityblock=manhatten','cosine','chebychev','jaccard','minkowski','manhattan','binary'
(scalar) initial step size, 0.5 by default
(scalar) initial radius of influence, 3*max(std(D)) by default
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.
A n by OutputDimension matrix containing coordinates of the projected points.
Demartines, P., Herault, J., "Curvilinear Component Analysis: a Self-Organizing Neural Network for Nonlinear Mapping of Data Sets", IEEE Transactions on Neural Networks, vol 8, no 1, 1997, pp. 148-154.