  Given labeled data represented by a binary matrix, we consider the task to
derive a Boolean matrix factorization which identifies commonalities and
specifications among the classes. While existing works focus on rank-one
factorizations which are either specific or common to the classes, we derive
class-specific alterations from common factorizations as well. Therewith, we
broaden the applicability of our new method to datasets whose
class-dependencies have a more complex structure. On the basis of synthetic and
real-world datasets, we show on the one hand that our method is able to filter
structure which corresponds to our model assumption, and on the other hand that
our model assumption is justified in real-world application. Our method is
parameter-free.
