Graph Based Multiway Cut algorithm
In this paper, we present a new graph based semi-supervised learning algorithm, using multiway cut
on a neighborhood graph to achieve an optimum classification. We also present a graph based feature
selection utilizing the global structure of the graph derived from both labeled and unlabeled
examples to construct a more adaptive graph for the multiway cut. With respect to the experiments
we conducted, both of our approaches are proved to have a promising performance on the improvement
of the learning accuracy.
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