Analysis and Optimization of Graph Decompositions by Lifted Multicuts

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review

Contributors

  • Andrea Horňáková - , Max Planck Institute for Informatics (Author)
  • Jan-Hendrik Lange - , Max Planck Institute for Informatics (Author)
  • Bjoern Andres - , Max Planck Institute for Informatics (Author)

Abstract

We study the set of all decompositions (clusterings) of a graph through its characterization as a set of lifted multicuts. This leads us to practically relevant insights related to the definition of classes of decompositions by must-join and must-cut constraints and related to the comparison of clusterings by metrics. To find optimal decompositions defined by minimum cost lifted multicuts, we establish some properties of some facets of lifted multicut polytopes, define efficient separation procedures and apply these in a branch-and-cut algorithm.

Details

Original languageEnglish
Title of host publicationProceedings of the 34th International Conference on Machine Learning
EditorsDoina Precup, Yee Whye Teh
Pages1539-1548
Volumeabs/1503.03791
Publication statusPublished - 2017
Peer-reviewedYes
Externally publishedYes

Publication series

SeriesProceedings of Machine Learning Research
Volume70

External IDs

dblp journals/corr/Andres15
ORCID /0000-0001-5036-9162/work/162346198

Keywords