I have a complex pipeline for predictive modeling of text, where the non-negative matrix factorization (NMF) is one part. I would like to evaluate the performance of the NMF independently of the neural network model that it is fed into afterwards. This means that I would like to evaluate the NMF in an unsupervised manner without any labels. In particular, I want to find a fitting value for the L1/L2 regularization term, alpha, in http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html.
It is important for me to optimize this regularization parameter as I want to use the NMF to remove noise in my dataset before feeding it into a classifier. What method/measure can I use to find the best performing value for alpha?