Partial Optimality in Cubic Correlation Clustering
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.
Cite
Text
Stein et al. "Partial Optimality in Cubic Correlation Clustering." International Conference on Machine Learning, 2023.Markdown
[Stein et al. "Partial Optimality in Cubic Correlation Clustering." International Conference on Machine Learning, 2023.](https://mlanthology.org/icml/2023/stein2023icml-partial/)BibTeX
@inproceedings{stein2023icml-partial,
title = {{Partial Optimality in Cubic Correlation Clustering}},
author = {Stein, David and Di Gregorio, Silvia and Andres, Bjoern},
booktitle = {International Conference on Machine Learning},
year = {2023},
pages = {32598-32617},
volume = {202},
url = {https://mlanthology.org/icml/2023/stein2023icml-partial/}
}