Kyoungup Park

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We concern the problem of learning a Mahalanobis distance metric for improving nearest neighbor classification. Our work is built upon the large margin nearest neighbor (LMNN) classification framework. Due to the semidefiniteness constraint in the optimization problem of LMNN, it is not scalable in terms of the dimensionality of the input data. The original(More)
Pairwise Markov random fields are an effective framework for solving many pixel labeling problems in computer vision. However, their performance is limited by their inability to capture higher-order correlations. Recently proposed higher-order models are showing superior performance to their pairwise counterparts. In this paper, we derive two variants of(More)
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