I have a training set of dimensions 126 x 5 (126 rows and 5 columns). I applied PCA on it and decided to keep the first 2 PCs. So I now have a matrix (say FeatureVector) of dimensions (126 x 2) onto which I want to project my training set scaled and normalized (say DataAdjust).
The source(page 16) I've found say to do this:
FinalData = T(FeatureVector) x T(DataAdjust)
but this would mean (multiplying dimensions): (2 x 126) x ( 5 x 126) which is not doable in matrix multiplications. Also, I think I should be getting a 126 x 2 matrix as FinalData.
My confusion came from the fact that I computed the principal component analysis with the PCA function from FactoMineR which already gives the training set projected onto the PCs space in the
res_pca$ind$coord variable. I manually calculated the eigenvectors of the covariance matrix of my dataset and applying, with these vectors, the bold formula above I got exactly the matrix