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[P] = cca(D, P, epochs, Mdist, alpha0, lambda0)

CCA Projects data vectors using Curvilinear Component Analysis.

 P = cca(D, P, epochs, [Dist], [alpha0], [lambda0])

  P = cca(D,2,10);           % projects the given data to a plane
  P = cca(D,pcaproj(D,2),5); % same, but with PCA initialization
  P = cca(D, 2, 10, Dist);   % same, but the given distance matrix is used
  Input and output arguments ([]'s are optional):
   D          (matrix) the data matrix, size dlen x dim
              (struct) data or map struct            
   P          (scalar) output dimension
              (matrix) size dlen x odim, the initial projection
   epochs     (scalar) training length
   [Dist]     (matrix) pairwise distance matrix, size dlen x dlen.
                       If the distances in the input space should
                       be calculated otherwise than as euclidian
                       distances, the distance from each vector
                       to each other vector can be given here,
                       size dlen x dlen. For example PDIST
                       function can be used to calculate the
                       distances: Dist = squareform(pdist(D,'mahal'));
   [alpha0]   (scalar) initial step size, 0.5 by default
   [lambda0]  (scalar) initial radius of influence, 3*max(std(D)) by default
   P          (matrix) size dlen x odim, the projections

 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).


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