Generative local metric learning for kernel regression

Yung-Kyun Noh, Masashi Sugiyama, Kee Eung Kim, Frank C. Park, Daniel D. Lee

Research output: Contribution to journalConference article

1 Scopus citations

Abstract

This paper shows how metric learning can be used with Nadaraya-Watson (NW) kernel regression. Compared with standard approaches, such as bandwidth selection, we show how metric learning can significantly reduce the mean square error (MSE) in kernel regression, particularly for high-dimensional data. We propose a method for efficiently learning a good metric function based upon analyzing the performance of the NW estimator for Gaussian-distributed data. A key feature of our approach is that the NW estimator with a learned metric uses information from both the global and local structure of the training data. Theoretical and empirical results confirm that the learned metric can considerably reduce the bias and MSE for kernel regression even when the data are not confined to Gaussian.

Original languageEnglish
Pages (from-to)2453-2463
Number of pages11
JournalAdvances in Neural Information Processing Systems
Volume2017-December
StatePublished - 2017 Jan 1
Event31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States
Duration: 2017 Dec 42017 Dec 9

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    Noh, Y-K., Sugiyama, M., Kim, K. E., Park, F. C., & Lee, D. D. (2017). Generative local metric learning for kernel regression. Advances in Neural Information Processing Systems, 2017-December, 2453-2463.