Principal surfaces from unsupervised kernel regression
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 practic...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 27(2005), 9 vom: 08. Sept., Seite 1379-91 |
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| Weitere Verfasser: | , , |
| Format: | Aufsatz |
| Sprache: | English |
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2005
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Evaluation Study Journal Article Research Support, Non-U.S. Gov't |
| Zusammenfassung: | 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 |
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| Beschreibung: | Date Completed 12.10.2005 Date Revised 10.12.2019 published: Print Citation Status MEDLINE |
| ISSN: | 1939-3539 |