C-salt: mining class-specific alterations in boolean matrix factorization

Sibylle Hess; Katharina Morik

doi:10.1007/978-3-319-71249-9_33

C-salt: mining class-specific alterations in boolean matrix factorization

Sibylle Hess
, Katharina Morik

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

10 Citations (Scopus)

41 Downloads (Pure)

Abstract

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.

Original language	English
Title of host publication	Joint European Conference on Machine Learning and Knowledge Discovery in Databases
Editors	Michelangelo Ceci, Saso Dzeroski, Celine Vens, Ljupco Todorovski, Jaakko Hollmen
Place of Publication	Cham
Publisher	Springer
Pages	547-563
Number of pages	17
ISBN (Electronic)	978-3-319-71249-9
ISBN (Print)	978-3-319-71248-2
DOIs	https://doi.org/10.1007/978-3-319-71249-9_33
Publication status	Published - 2017
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10534 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords

Boolean matrix factorization
Nonconvex optimization
Proximal alternating linearized optimization
Shared subspace learning

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10.1007/978-3-319-71249-9_33

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Hess, S., & Morik, K. (2017). C-salt: mining class-specific alterations in boolean matrix factorization. In M. Ceci, S. Dzeroski, C. Vens, L. Todorovski, & J. Hollmen (Eds.), Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 547-563). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10534 LNAI). Springer. https://doi.org/10.1007/978-3-319-71249-9_33

Hess, Sibylle ; Morik, Katharina. / C-salt : mining class-specific alterations in boolean matrix factorization. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. editor / Michelangelo Ceci ; Saso Dzeroski ; Celine Vens ; Ljupco Todorovski ; Jaakko Hollmen. Cham : Springer, 2017. pp. 547-563 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Hess, S & Morik, K 2017, C-salt: mining class-specific alterations in boolean matrix factorization. in M Ceci, S Dzeroski, C Vens, L Todorovski & J Hollmen (eds), Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10534 LNAI, Springer, Cham, pp. 547-563. https://doi.org/10.1007/978-3-319-71249-9_33

C-salt: mining class-specific alterations in boolean matrix factorization. / Hess, Sibylle; Morik, Katharina.
Joint European Conference on Machine Learning and Knowledge Discovery in Databases. ed. / Michelangelo Ceci; Saso Dzeroski; Celine Vens; Ljupco Todorovski; Jaakko Hollmen. Cham: Springer, 2017. p. 547-563 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10534 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

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AU - Hess, Sibylle

AU - Morik, Katharina

PY - 2017

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AB - 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.

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KW - Nonconvex optimization

KW - Proximal alternating linearized optimization

KW - Shared subspace learning

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 563

BT - Joint European Conference on Machine Learning and Knowledge Discovery in Databases

A2 - Ceci, Michelangelo

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Hess S, Morik K. C-salt: mining class-specific alterations in boolean matrix factorization. In Ceci M, Dzeroski S, Vens C, Todorovski L, Hollmen J, editors, Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer. 2017. p. 547-563. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-71249-9_33

C-salt: mining class-specific alterations in boolean matrix factorization

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