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Dissimilarity in Graph-Based Semi-Supervised Classification

Andrew B. Goldberg, Xiaojin Zhu, Stephen Wright; JMLR W&P 2:155-162, 2007.

Abstract

Label dissimilarity specifies that a pair of examples probably have different class labels. We present a semi-supervised classification algorithm that learns from dissimilarity and similarity information on labeled and unlabeled data. Our approach uses a novel graphbased encoding of dissimilarity that results in a convex problem, and can handle both binary and multiclass classification. Experiments on several tasks are promising.



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