Title: Adaptive metric kernel regression
Author:
Cyril Goutte and
Jan Larsen
Department of Mathematical Modeling - Building 321
Technical University of Denmark, DK-2800 Lyngby, Denmark
Phone: +45 4525 3921,3923
Fax: +45 4587 2599
E-mail: cg,jl@imm.dtu.dk
http://eivind.imm.dtu.dk
Abstract
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.
Preprint, appears in T. Constantinides, S.-Y. Kung, M. Niranjan and
E. Wilson (eds), Neural Networks for Signal Processing VIII -- Proceedings of the 1998 IEEE Workshop (NNSP'98, Cambridge), pp. 184-193.
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