Principal surfaces from unsupervised kernel regression
2005 | journal article. A publication with affiliation to the University of Göttingen.
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- Authors
- Meinicke, Peter ; Klanke, S.; Memisevic, R.; Ritter, H.
- 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.
- Issue Date
- 2005
- Status
- published
- Publisher
- Ieee Computer Soc
- Journal
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- ISSN
- 0162-8828