Generative Local Metric Learning for Kernel Regression

NeurIPS 2017 Yung-Kyun NohMasashi SugiyamaKee-Eung KimFrank ParkDaniel D. Lee

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... (read more)

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