THE MATLAB CODE OF CANONICAL CORRELATION ANALYSIS (CCA)

THE MATLAB CODE OF CANONICAL CORRELATION ANALYSIS (CCA)

转自：http://www.cppblog.com/guijie/archive/2015/06/02/210807.html

%Reference: the matlab code of "CVPR 2011 Iterative Quantization". I have checked the correctness of the corresponding derivation and matlab code. No need to check again. Just use it

function [Wx, r] = cca(X,Y,reg)

%

% X is input data for the 1st view (image), n*d, n images, d dim

% Y is input data for the 2nd view (Tag), n*D, D dim

% reg is regularization parameter, usually set by validation

% in our work, reg = 0.0001 which works well

%

% Wx is the embedding function for image

% r is the eigenvalue

%

%

% to run the code, you need visual data X and tag data Y

% 

% bit = 32, 64, 128 ...
% [eigenvector,r] = cca(X, Y, 0.0001); % this computes CCA projections
% eigenvector = eigenvector(:,1:bit)*diag(r(1:bit)); % this performs a scaling using eigenvalues
% E = X*eigenvector; % final projection to obtain embedding E

%

z = [X, Y];

C = cov(z);

sx = size(X,2);

sy = size(Y,2);

Cxx = C(1:sx, 1:sx) + reg*eye(sx);

Cxy = C(1:sx, sx+1:sx+sy);

Cyx = Cxy';

Cyy = C(sx+1:sx+sy,sx+1:sx+sy) + reg*eye(sy);

Rx = chol(Cxx);

invRx = inv(Rx);

Z = invRx'*Cxy*(Cyy\Cyx)*invRx;

Z = 0.5*(Z' + Z); 

[Wx,r] = eig(Z);   % basis in h (X)

r = sqrt(real(r)); % as the original r we get is lamda^2

Wx = invRx * Wx;   % actual Wx values

r = diag(r);

[r index] = sort(r,'descend');

Wx = Wx(:,index);