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Generative Local Metric Learning for Nearest Neighbor Classification
Yung-Kyun Noh · Byoung-Tak Zhang · Daniel Lee

Mon Dec 06 12:00 AM -- 12:00 AM (PST) @

We consider the problem of learning a local metric to enhance the performance of nearest neighbor classification. Conventional metric learning methods attempt to separate data distributions in a purely discriminative manner; here we show how to take advantage of information from parametric generative models. We focus on the bias in the information-theoretic error arising from finite sampling effects, and find an appropriate local metric that maximally reduces the bias based upon knowledge from generative models. As a byproduct, the asymptotic theoretical analysis in this work relates metric learning with dimensionality reduction, which was not understood from previous discriminative approaches. Empirical experiments show that this learned local metric enhances the discriminative nearest neighbor performance on various datasets using simple class conditional generative models.

Author Information

Yung-Kyun Noh (Hanyang University / Korea Institute for Advanced Study)
Byoung-Tak Zhang (Seoul National University)
Daniel Lee (Samsung Research/Cornell University)

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