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Principal surfaces from unsupervised kernel regression

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4 Author(s)
Meinicke, P. ; Dept. of Bioinformatics, Gottingen Univ., Germany ; Klanke, S. ; Memisevic, R. ; Ritter, H.

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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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:27 ,  Issue: 9 )