What is a fast way to compute column by column correlation in matlab

Question

I have two very large matrices (60x25000) and I\'d like to compute the correlation between the columns only between the two matrices. For example:

corrVal(1) = c         


        

Answer 1

I can obtain a x100 speed improvement by computing it by hand.

An=bsxfun(@minus,A,mean(A,1)); %%% zero-mean
Bn=bsxfun(@minus,B,mean(B,1)); %%% zero-mean
An=bsxfun(@times,An,1./sqrt(sum(An.^2,1))); %% L2-normalization
Bn=bsxfun(@times,Bn,1./sqrt(sum(Bn.^2,1))); %% L2-normalization
C=sum(An.*Bn,1); %% correlation

You can compare using that code:

A=rand(60,25000);
B=rand(60,25000);

tic;
C=zeros(1,size(A,2));
for i = 1:size(A,2)
    C(i)=corr(A(:,i), B(:,i));
end
toc; 

tic
An=bsxfun(@minus,A,mean(A,1));
Bn=bsxfun(@minus,B,mean(B,1));
An=bsxfun(@times,An,1./sqrt(sum(An.^2,1)));
Bn=bsxfun(@times,Bn,1./sqrt(sum(Bn.^2,1)));
C2=sum(An.*Bn,1);
toc
mean(abs(C-C2)) %% difference between methods

Here are the computing times:

Elapsed time is 10.822766 seconds.
Elapsed time is 0.119731 seconds.

The difference between the two results is very small:

mean(abs(C-C2))

ans =
  3.0968e-17

EDIT: explanation

bsxfun does a column-by-column operation (or row-by-row depending on the input).

An=bsxfun(@minus,A,mean(A,1));

This line will remove (@minus) the mean of each column (mean(A,1)) to each column of A... So basically it makes the columns of A zero-mean.

An=bsxfun(@times,An,1./sqrt(sum(An.^2,1)));

This line multiply (@times) each column by the inverse of its norm. So it makes them L-2 normalized.

Once the columns are zero-mean and L2-normalized, to compute the correlation, you just have to make the dot product of each column of An with each column of B. So you multiply them element-wise An.*Bn, and then you sum each column: sum(An.*Bn);.