Previously, convergence was achieved when |W[i, j]-preW[i, j]|<=tol for all i, j.
Running NMF on 10*X (where X is the data matrix ) would increase the number of iterations compared to NMF on X. Now, ||W[:, j]-preW[:, j]||/||W[:, j]+preW[:, j]||<=tol for all j is the new convergence criterion, which is relative difference rather absolute difference and thus, it is scale invariant.
This might be a breaking change but it is unclear.
Previously, convergence was achieved when
|W[i, j]-preW[i, j]|<=tol for all i, j.Running NMF on
10*X(whereXis the data matrix ) would increase the number of iterations compared to NMF onX. Now,||W[:, j]-preW[:, j]||/||W[:, j]+preW[:, j]||<=tol for all jis the new convergence criterion, which is relative difference rather absolute difference and thus, it is scale invariant.This might be a breaking change but it is unclear.
This new criterion is more similar to the the stop condition in
sklearn.decomposition.NMF