PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Leave-K-Out Cross-Validation Scheme for Unsupervised Kernel Regression
Stefan Klanke and Helge Ritter
In: International Conference on Artificial Neural Networks (ICANN) 2006, 10-14 September 2006, Athens, Greece.

Abstract

We show how to employ leave-K-out cross-validation in Unsupervised Kernel Regression, a recent method for learning of nonlinear manifolds. We thereby generalize an already present regularization method, yielding more flexibility without additional computational cost. We demonstrate our method on both toy and real data.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3428
Deposited By:Stefan Klanke
Deposited On:10 February 2008