Journal article
Adaptive metric kernel regression
Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate regression by minimising a cross-validation estimate of the generalisation error.
This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard approach. Finally, we benchmark the method using the DELVE environment.
Language: | English |
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Publisher: | Kluwer Academic Publishers |
Year: | 2000 |
Pages: | 155-167 |
ISSN: | 1573109x and 09225773 |
Types: | Journal article |
DOI: | 10.1023/A:1008159803952 |
ORCIDs: | Larsen, Jan |
Circuits and Systems Computer Imaging, Vision, Pattern Recognition and Graphics Electrical Engineering Engineering Gaussian Process General Regression Neural Network Image Processing and Computer Vision Input Dimension Multivariate Adaptive Regression Spline Pattern Recognition Signal, Image and Speech Processing Smoothing Parameter