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covriance
2003 Feb 06
6
Confused by SVD and Eigenvector Decomposition in PCA
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;
[EVector,EValues]=eig(C) %%
L = diag(EValues) %%L is a column vector with eigenvalues as the elements
percent = L(1)/sum(L);
Others argue using Sigular Value Decomposition(SVD) to
calculate the same quantity, as:
[U,S,V]=svd(X);
L = diag(S);
L = L.^2;
perce...