Different Result between using PCA from toolbox and using manually programmed PCA
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I try to compute PCA on my data. First, I do PCA on the data using function from toolbox. I also do PCA on the data using manual programmed function based my knowledge. First, I calculate covariance matrix of the data. Then, I find its eigenvalue and eigenvector.
PCA using function from toolbox:
[COEFF,SCORE,latent] = princomp(allData);
PCA using manually programmed function:
[V,D]=eig(cov(allData));
Both of those methods yield matrices called coefficient matrix, COEFF for the first and V for the second. Both have exactly same value, but have, sometime, different sign. Can someone explain to me?
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Wei Wang
2012 年 11 月 28 日
PCA enforces a sign convention on the coefficients. The largest element in each column will have a positive sign.
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