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MDS_CS Trainable mapping for classical scaling

    W = MDS_CS(D,N)
    W = D*MDS_CS([],N)
    W = D*MDS_CS(N)

 D Square dissimilarity matrix of the size M x M
 N Desired output dimensionality (optional; default: 2)

 W Classical scaling mapping


A linear mapping of objects given by a symmetric distance matrix D with  a zero diagonal onto an N-dimensional Euclidean space such that the square  distances are preserved as much as possible.

D is assumed to approximate the Euclidean distances, i.e.  D_{ij} = sqrt(sum_k (x_i-x_j)^2).


1. I. Borg and P. Groenen, Modern Multidimensional Scaling, Springer Verlag, Berlin, 1997.
2. T.F. Cox and M.A.A. Cox, Multidimensional Scaling, Chapman and Hall, London, 1994.

See also

mappings, mds_init, mds_cs,

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