Speed-efficient classification for complex vectors in MATLAB

I am trying to optimize this piece of code and get rid of the nested loop implemented. I am finding difficulties in applying a matrix to pdist function

For example, 1+j // -1+j // -1+j // -1-j are the initial points and i am trying to detect 0.5+0.7j to with point it belong by min distance approach .
any help is appreciated

function result = minDisDetector( newPoints, InitialPoints)
result = [];
for i=1:length(newPoints)
    minDistance = Inf;
    for j=1:length(InitialPoints)

        X = [real(newPoints(i)) imag(newPoints(i));real(InitialPoints(j)) imag(InitialPoints(j))];
        d = pdist(X,'euclidean');

        if d < minDistance
            minDistance = d;
            index = j;
        end
    end
    result = [result; InitialPoints(index)]; 
end     
end

You can use efficient euclidean distance calculation as listed in Speed-efficient classification in Matlab for a vectorized solution -

%// Setup the input vectors of real and imaginary into Mx2 & Nx2 arrays
A = [real(InitialPoints) imag(InitialPoints)];
Bt = [real(newPoints).' ; imag(newPoints).'];

%// Calculate squared euclidean distances. This is one of the vectorized
%// variations of performing efficient euclidean distance calculation using 
%// matrix multiplication linked earlier in this post.
dists = [A.^2 ones(size(A)) -2*A ]*[ones(size(Bt)) ; Bt.^2 ; Bt];

%// Find min index for each Bt & extract corresponding elements from InitialPoints
[~,min_idx] = min(dists,[],1);
result_vectorized = InitialPoints(min_idx);

Quick runtime tests with newPoints as 400 x 1 & InitialPoints as 1000 x 1:

-------------------- With Original Approach
Elapsed time is 1.299187 seconds.
-------------------- With Proposed Approach
Elapsed time is 0.000263 seconds.