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 Kn’);
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);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 Kn’);
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); 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 Kn’);
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));

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