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## Main Question or Discussion Point

Hi all,

I thought I posted this last night but have recieved no notification of it being moved or cant find it the thread I have started list.

I was wondering if you could help me understand how PCA, principal component analysis, works a little better. I have read often that it to get the best results using PCA you should mean centre the variables within your matrix first. I thought however that one method of calculating the principal components was the covariation matrix method where the eigenvalues and eigenvectors gives you the direction of the greatest variance within the matrix. I also assumed that the elements of the covariation matrix was calculated by using the following formula:

Cov=sum([Xi-Xmean][Yi-Ymean])/N-1

If i subtracted the mean from the original data matrix would it matter because I would get the same distribution regardless using the above calculation.

I hope some one can help

Thanks

I thought I posted this last night but have recieved no notification of it being moved or cant find it the thread I have started list.

I was wondering if you could help me understand how PCA, principal component analysis, works a little better. I have read often that it to get the best results using PCA you should mean centre the variables within your matrix first. I thought however that one method of calculating the principal components was the covariation matrix method where the eigenvalues and eigenvectors gives you the direction of the greatest variance within the matrix. I also assumed that the elements of the covariation matrix was calculated by using the following formula:

Cov=sum([Xi-Xmean][Yi-Ymean])/N-1

If i subtracted the mean from the original data matrix would it matter because I would get the same distribution regardless using the above calculation.

I hope some one can help

Thanks