About PCA and 'manual' decomposition

If I try to perform a PCA with elementary functions, I use this code (MATLAB 7.10):

load acetylene.mat

dataset = [x1 x2 x3 y];

[M,N]=size(dataset);

mn=mean(dataset,1);

data=dataset-repmat(mn,M,1);

covariance = 1 / (M-1) * (data')*(data);

[PrinComp,L]=eig(covariance);

L=diag(L);

[values,Lind]=sort(-1*L);

PC = PC(:,Lind);

At this point I tried to compare what I've done with: [a,b]=PCA(dataset). Well, eigenvalues are the same, but the matrix is a little bit different (the second column has the opposite signs).

Thanks.

Answer 1

[Thorsten]

This is discussed here: http://stackoverflow.com/questions/18152052/matlab-eig-returns-inverted-signs-sometimes

[Damien]

Thanks. It is just a matter of non-unique solution, but I wonder why, being the same functions...