  The recently proposed unlabeled-unlabeled (UU) classification method allows
us to train a binary classifier only from two unlabeled datasets with different
class priors. Since this method is based on the empirical risk minimization, it
works as if it is a supervised classification method, compatible with any model
and optimizer. However, this method sometimes suffers from severe overfitting,
which we would like to prevent in this paper. Our empirical finding in applying
the original UU method is that overfitting often co-occurs with the empirical
risk going negative, which is not legitimate. Therefore, we propose to wrap the
terms that cause a negative empirical risk by certain correction functions.
Then, we prove the consistency of the corrected risk estimator and derive an
estimation error bound for the corrected risk minimizer. Experiments show that
our proposal can successfully mitigate overfitting of the UU method and
significantly improve the classification accuracy.
