Bayesian Neighborhood Component Analysis
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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
关键字：Bayes modeling distance metric learning label noise neighborhood component analysis
摘要：Learning a distance metric in feature space potentially improves the performance of the K nearest neighbor classifier and is useful in many real-world applications. Many metric learning (ML) algorithms are, however, based on the point estimation of a quadratic optimization problem, which is time-consuming, susceptible to overfitting, and lacks a natural mechanism to reason with parameter uncertainty-a property useful especially when the training set is small and/or noisy. To deal with these issues, we present a novel Bayesian ML (BML) method, called Bayesian neighborhood component analysis (NCA), based on the well-known NCA method, in which the metric posterior is characterized by the local label consistency constraints of observations, encoded with a similarity graph instead of independent pairwise constraints. For efficient Bayesian inference, we explore the variational lower bound over the log-likelihood of the original NCA objective. Experiments on several publicly available data sets demonstrate that the proposed method is able to learn robust metric measures from small size data set and/or from challenging training set with labels contaminated by errors. The proposed method is also shown to outperform a previous pairwise constrained BML method.
ISSN号：2162-237X
是否译文：否
发表时间：2018-07-01
合写作者：谭晓阳
通讯作者：谭晓阳,王东生