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Hi Feng,
AFIK SVD analysis provides a one-step method for computing all the
components of the eigen value problem, without the need to compute and
store big covariance matrices. And also the resulting decomposition is
computationally more stable and robust.
Cheers,
Antonio Rodriguez
-
Thanks for those replies.
But I tested several cases, and found the two
percentage from SVD and EVD are not
the same.
So how to explain the difference and which
one should be the right one for use
in PCA?
- Original Message -
From: antonio rodriguez [EMAIL PROTECTED]
To: Feng Zhang
I used Matlab to do this case study.
x = randn(200,3); %%generating a 200x3 Gaussian matrix
[a,b,c]=svd(x); %%SVD composition
S=diag(b)
S =[15.6765 14.8674 13.4016]'
S(1)^2/sum(S.^2);
0.3802
ZeroedX = X - repmat(mean(X),200,1); %%ZeroedX is now zero centered data
C = cov(ZeroedX);
Hey, All
In principal component analysis (PCA), we want to know how many percentage
the first principal component explain the total variances among the data.
Assume the data matrix X is zero-meaned, and
I used the following procedures:
C = covriance(X) %% calculate the covariance matrix;
If I'm not mistaken, for positive semi-definite matrices, the eigenvalues
are equal to squared singular values, so you should get the same answer
either way.
The code you shown is definitely not R (looks like Matlab), so why are you
posting to R-help?
Andy
-Original Message-
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