I am working on singular value decomposition (SVD) and principal component analysis (PCA). I use these methods to analyse a same dataset.
What I do is mean center the dataset first, and then input the mean centered dataset into PCA and SVD respectively to extract features. According to the formulas, I was expecting slightly difference between result from these two solutions.
The results show that the resulting components of PCA and SVD are significantly different, that some pair of the corresponding components may have an angle of 60 degrees, though the eigenvalues of components are the same.
May I know if this is normal or I did something wrong?