Analyzing PlanarCC: Demonstrating the Equivalence of PlanarCC and The Multi-Cut LP Relaxation
نویسنده
چکیده
Correlation clustering is an exciting area of research in the fields of graphical models and image segmentation. In this article we study the linear programming (LP) relaxation corresponding to PlanarCC and the multi-cut LP relaxation which are two methods for correlation clustering. We demonstrate that they have equal value when optimized. This provides justification for the use of PlanarCC which is very fast on planar graphs in situations where the multi-cult LP relaxation is currently employed.
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تاریخ انتشار 2014