학술논문
Principal surfaces from unsupervised kernel regression
Document Type
Periodical
Author
Source
IEEE Transactions on Pattern Analysis and Machine Intelligence IEEE Trans. Pattern Anal. Mach. Intell. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 27(9):1379-1391 Sep, 2005
Subject
Language
ISSN
0162-8828
2160-9292
1939-3539
2160-9292
1939-3539
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.