Abstract
We propose a new outer relaxation of the multicut polytope, along with a dual decomposition approach for correlation clustering and multicut segmentation, for general graphs. Each subproblem is a minimum \(st\)-cut problem and can thus be solved efficiently. An optimal reparameterization is found using subgradients and affords a new characterization of the basic LP relaxation of the multicut problem, as well as informed decoding heuristics. The algorithm we propose for solving the problem distributes the computation and is amenable to a parallel implementation.
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Yarkony, J., Beier, T., Baldi, P., Hamprecht, F.A. (2015). Parallel Multicut Segmentation via Dual Decomposition. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2014. Lecture Notes in Computer Science(), vol 8983. Springer, Cham. https://doi.org/10.1007/978-3-319-17876-9_4
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DOI: https://doi.org/10.1007/978-3-319-17876-9_4
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