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

Sibylle Hess, Katharina Morik

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

4 Citations (Scopus)
7 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 languageEnglish
Title of host publicationJoint European Conference on Machine Learning and Knowledge Discovery in Databases
EditorsMichelangelo Ceci, Saso Dzeroski, Celine Vens, Ljupco Todorovski, Jaakko Hollmen
Place of PublicationCham
PublisherSpringer
Pages547-563
Number of pages17
ISBN (Electronic)978-3-319-71249-9
ISBN (Print)978-3-319-71248-2
DOIs
Publication statusPublished - 2017
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10534 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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