【摘 要】
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We present variational methods for semi-supervised and unsupervised data classification involving graph cuts,which are equivalent to minimizing the graph total variation of an assignment function.We d
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We present variational methods for semi-supervised and unsupervised data classification involving graph cuts,which are equivalent to minimizing the graph total variation of an assignment function.We discuss performance and efficiency of various algorithms for such problems including max flow,augmented Lagrangian,and geometric methods such as the MBO scheme and Allen-Cahn flow.
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