关键词: k近邻, 测度学习, 生成模型, 区分模型

Abstract: Previous work on metric learning for k Nearest Neighbor（kNN） has focused on purely discriminative approach.A approach is proposed to learn a metric by analyzing the probability distribution on nearest neighbors provided that the underlying generative model is known.Experiments show that this learned local metric can improve the performance of the discriminative kNN approach using simple class conditional generative model.

Key words: k nearest neighbor, metric learning, generative model, discriminative model