title: Principal surfaces from unsupervised kernel regression.
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.
authors.affiliation: P. Meinicke is with the Bioinformatics Department, Faculty of Biology, University of Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany. pmeinic@gwdg.de
authors.forename: Peter
authors.initials: P
authors.lastname: Meinicke
authors.forename: Stefan
authors.initials: S
authors.lastname: Klanke
authors.forename: Roland
authors.initials: R
authors.lastname: Memisevic
authors.forename: Helge
authors.initials: H
authors.lastname: Ritter
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis