Partial Optimality in Cubic Correlation Clustering.
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
The higher-order correlation clustering problem is an expressive model, and recently, local search heuristics have been proposed for several applications. Certifying optimality, however, is NP-hard and practically hampered already by the complexity of the problem statement. Here, we focus on establishing partial optimality conditions for the special case of complete graphs and cubic objective functions. In addition, we define and implement algorithms for testing these conditions and examine their effect numerically, on two datasets.
Details
Original language | English |
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Title of host publication | Proceedings of the 40th International Conference on Machine Learning |
Pages | 32598-32617 |
Number of pages | 20 |
Publication status | Published - 2023 |
Peer-reviewed | Yes |
Publication series
Series | Proceedings of Machine Learning Research |
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Volume | 202 |
External IDs
ORCID | /0000-0001-5036-9162/work/143781902 |
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Scopus | 85174390005 |