Different kind of normalization

I have read in Matlab that normalization of a vector is u/norm(u).

However, I have a matrix (N x N)where the columns are different vectors. I want for each element of column vectors to do something like: (u(i) - mean(u))/std(u) without looping so that at the end of it each column vectors are bunch of standard normals.

Is there a standard way to do it in matlab or do I really have to code the loop.

Answer 1

[Oleg Komarov]

% Create standard normal distributed samples with std = 100;
A = randn(100000,10)*100;
% Normalize
B = bsxfun(@rdivide,bsxfun(@minus,A,mean(A)), std(A));
% Check first column
hist(B(:,1),100)

Note that mean and std operate along rows, i.e. for a matrix they give a result for each column.

Oleg

[Zeinab]

Thanks!

Answer 2

[Matt Tearle]

If you have Statistics Toolbox, use uhat = zscore(u).

Answer 3

[Steven Lord]

Use the normalize function.