Distance Metric Learning under Covariate Shift

  title={Distance Metric Learning under Covariate Shift},
  author={Bin Cao and Xiaochuan Ni and Jian-Tao Sun and Gang Wang and Qiang Yang},
Learning distance metrics is a fundamental problem in machine learning. Previous distance-metric learning research assumes that the training and test data are drawn from the same distribution, which may be violated in practical applications. When the distributions differ, a situation referred to as covariate shift, the metric learned from training data may not work well on the test data. In this case the metric is said to be inconsistent. In this paper, we address this problem by proposing a… CONTINUE READING


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