C-SALT:
Mining Class-Specific ALTerations in Boolean Matrix Factorization
Sibylle Hess, Katharina Morik
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.
Requirements:
- 64 Bit Linux
- CUDA 7.5
- libjpeg (for JPEG to CSV conversion)
Download:
