I am working with a 6650254x5650 sparse matrix which values are in numpy.float64 format.
I am using the NMF implemetnation from scikit-learn as following
from sklearn.decomposition import NMF
model = NMF(n_components=12, init='random', random_state=0, max_iter=20, l1_ratio=0.01)
W = model.fit_transform(X_all_sparse, )
H = model.components_
W
It seems for larger number of n_components I get W matrices where all elements are NaN. For example if n_components is larger than 7 - but it works when n_components is 19 ! I wonder what can cause this and what are the other libraries that can handle such big matrices efficiently that I can benchmark against.
update If other have a similar problem, meanwhile, I am using the implicit library