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InstagramCapture_fc802113-cc01-47a2-91b5-700366dac5b4.png Email: sibylle.hess tu-dortmund.de
Phone: 0231/755-5107
Fax: 0231/755-5105
Room-No.: OH12 R4.011

Research Topics

Publications

Hess, Sibylle and Morik, Katharina and Piatkowski, Nico. . In http://dx.doi.org/10.1007/s10618-017-0508-z (editors), Data Mining and Knowledge Discovery, pages 1--42, 2017.
Morik, Katharina and Jung, Alexander and Weckwerth, Jan and Rötner, Stefan and Hess, Sibylle and Buschjäger, Sebastian and Pfahler, Lukas. . No. 2, 2015.
Hess, Sibylle. . TU Dortmund, 2015.
Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017 Hess, Sibylle and Morik, Katharina. . In https://link.springer.com/content/pdf/10.1007/978-3-319-71249-9_33.pdf (editors), Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, 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., Springer, 2017.

Software