The nature of statistical learning theory
The nature of statistical learning theory
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning low-rank kernel matrices
ICML '06 Proceedings of the 23rd international conference on Machine learning
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Fast solvers and efficient implementations for distance metric learning
Proceedings of the 25th international conference on Machine learning
Transferring localization models across space
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
A Kernel Approach for Semisupervised Metric Learning
IEEE Transactions on Neural Networks
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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 novel metric learning framework known as consistent distance metric learning (CDML), which solves the problem under covariate shift situations. We theoretically analyze the conditions when the metrics learned under covariate shift are consistent. Based on the analysis, a convex optimization problem is proposed to deal with the CDML problem. An importance sampling method is proposed for metric learning and two importance weighting strategies are proposed and compared in this work. Experiments are carried out on synthetic and real world datasets to show the effectiveness of the proposed method.