In the original Sparse PCA paper
Sparse Principal Component Analysis ZOU, HASTIE, TIBSHIRANI
they describe a way to compute the adjusted variance explained by computing QR decomposition of the Z matrix (size n times k) where n is the number of observations and k is the low dimensional space. Then the adjusted variance is just trace(R^2).
I tried doing that for probabilistic sparse PCA that I implemented but it turns that the adj. variance explained is a very high number ~700. We know that it should be between 0 and 1, so probably I am missing some normalization constant. Does anyone have any idea on what could be going wrong or how to normalize.
My original data dimensions are 100*50 i.e. n=100, p=50 and k=10