PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Principal Surfaces from Unsupervised Kernel Regression
Peter Meinicke, Stefan Klanke, Roland Memisevic and Helge Ritter
IEEE Trans. on Pattern Analysis and Machine Intelligence Volume 27, Number 9, pp. 1379-1391, 2005.

Abstract

We propose a nonparametric approach to learning of principal surfaces based on an unsupervised formulation of the Nadaraya-Watson kernel regression estimator. As compared with previous approaches to principal curves and surfaces, the new method offers several advantages: First, it provides a practical solution to the model selection problem because all parameters can be estimated by leave-one-out cross-validation without additional computational cost. In addition, our approach allows for a convenient incorporation of nonlinear spectral methods for parameter initialization, beyond classical initializations based on linear PCA. Furthermore, it shows a simple way to fit principal surfaces in general feature spaces, beyond the usual data space setup. The experimental results illustrate these convenient features on simulated and real data.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3423
Deposited By:Stefan Klanke
Deposited On:10 February 2008