cca.rar_CCA团簇团簇_aggregation matlab_cca模型_cca簇团_簇团

function [Wx, Wy, r] = cca(X,Y) % CCA calculate canonical correlations % % [Wx Wy r] = cca(X,Y) where Wx and Wy contains the canonical correlation % vectors as columns and r is a vector with corresponding canonical % correlations. The correlations are sorted in descending order. X and Y % are matrices where each column is a sample. Hence, X and Y must have % the same number of columns. % % Example: If X is M*K and Y is N*K there are L=MIN(M,N) solutions. Wx is % then M*L, Wy is N*L and r is L*1. % % % ?2000 Magnus Borga, Link�pings universitet % --- Calculate covariance matrices --- z = [X;Y]; C = cov(z.'); sx = size(X,1); sy = size(Y,1); Cxx = C(1:sx, 1:sx) + 10^(-8)*eye(sx); Cxy = C(1:sx, sx+1:sx+sy); Cyx = Cxy'; Cyy = C(sx+1:sx+sy, sx+1:sx+sy) + 10^(-8)*eye(sy); invCyy = inv(Cyy); % --- Calcualte Wx and r --- [Wx,r] = eig(inv(Cxx)*Cxy*invCyy*Cyx); % Basis in X r = sqrt(real(r)); % Canonical correlations % --- Sort correlations --- V = fliplr(Wx); % reverse order of eigenvectors r = flipud(diag(r)); % extract eigenvalues anr reverse their orrer [r,I]= sort((real(r))); % sort reversed eigenvalues in ascending order r = flipud(r); % restore sorted eigenvalues into descending order for j = 1:length(I) Wx(:,j) = V(:,I(j)); % sort reversed eigenvectors in ascending order end Wx = fliplr(Wx); % restore sorted eigenvectors into descending order % --- Calcualte Wy --- Wy = invCyy*Cyx*Wx; % Basis in Y Wy = Wy./repmat(sqrt(sum(abs(Wy).^2)),sy,1); % Normalize Wy