# cca - be calculated otherwise than as euclidian distances...

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SOM Toolbox Online documentation http://www.cis.hut.fi/projects/somtoolbox/ cca [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
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Unformatted text preview: 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). See also SAMMON, PCAPROJ. [ SOM Toolbox online doc ]...
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## This note was uploaded on 05/23/2010 for the course CS 245 taught by Professor Dunno during the Spring '10 term at Aarhus Universitet.

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