Optimize MATLAB code (nested for loop to compute similarity matrix)

Question

I am computing a similarity matrix based on Euclidean distance in MATLAB. My code is as follows:

for i=1:N % M,N is the size of the matrix x for whose elements I         


        

Answer 1

The function to do so in matlab is called pdist. Unfortunately it is painfully slow and doesnt take Matlabs vectorization abilities into account.

The following is code I wrote for a project. Let me know what kind of speed up you get.

   Qx=repmat(dot(x,x,2),1,size(x,1));
   D=sqrt(Qx+Qx'-2*x*x');

Note though that this will only work if your data points are in the rows and your dimensions the columns. So for example lets say I have 256 data points and 100000 dimensions then on my mac using x=rand(256,100000) and the above code produces a 256x256 matrix in about half a second.