can you please explain the logic of this code

function alphaKrow = mkpca(data,sigma,numev) % ----------------------------------------------------------------------- % global K

[n, d] = size(data); % n : number of data points % d : dimension of data points sigma = 2.5; % kernel matrix: K = zeros(n,n);

% kernel parameter: param = 0.5/(sigma*sigma);

% fprintf('computing kernel matrix K\n'); for i=1:n for j=i:n K(i,j) = kernel(data(i,:),data(j,:),param); K(j,i) = K(i,j); end end

% correct K for non-zero center of data in feature space: Krow = sum(K,1)/n; Ksum = sum(Krow)/n;

for i=1:n for j=1:n K(i,j) = K(i,j) - Krow(i) - Krow(j) + Ksum; end end

opts.disp = 0; [alpha,lambda] = eigs(K);

% normalize alpha: alpha = alpha * inv(sqrt(lambda));

% compute some helper vectors: alphaKrow = Krow * alpha; K = imresize(K,[256,256]); % ----------------------------------------------------------------------- % function k = kernel(x,y,param) % ----------------------------------------------------------------------- % diff = x-y; k = exp(-(diff * diff')*param);