Principal surfaces from unsupervised kernel regression

Meinicke P, Klanke S, Memisevic R, Ritter H (2005)
IEEE Transactions on Pattern Analysis and Machine Intelligence 27(9): 1379-1391.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
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Abstract / Bemerkung
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.
Erscheinungsjahr
Zeitschriftentitel
IEEE Transactions on Pattern Analysis and Machine Intelligence
Band
27
Zeitschriftennummer
9
Seite
1379-1391
ISSN
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Meinicke P, Klanke S, Memisevic R, Ritter H. Principal surfaces from unsupervised kernel regression. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005;27(9):1379-1391.
Meinicke, P., Klanke, S., Memisevic, R., & Ritter, H. (2005). Principal surfaces from unsupervised kernel regression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9), 1379-1391. doi:10.1109/TPAMI.2005.183
Meinicke, P., Klanke, S., Memisevic, R., and Ritter, H. (2005). Principal surfaces from unsupervised kernel regression. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1379-1391.
Meinicke, P., et al., 2005. Principal surfaces from unsupervised kernel regression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9), p 1379-1391.
P. Meinicke, et al., “Principal surfaces from unsupervised kernel regression”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, 2005, pp. 1379-1391.
Meinicke, P., Klanke, S., Memisevic, R., Ritter, H.: Principal surfaces from unsupervised kernel regression. IEEE Transactions on Pattern Analysis and Machine Intelligence. 27, 1379-1391 (2005).
Meinicke, Peter, Klanke, Stefan, Memisevic, R., and Ritter, Helge. “Principal surfaces from unsupervised kernel regression”. IEEE Transactions on Pattern Analysis and Machine Intelligence 27.9 (2005): 1379-1391.

6 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

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Aßhauer KP, Klingenberg H, Lingner T, Meinicke P., Int J Mol Sci 15(7), 2014
PMID: 25026170
Shared Kernel Information Embedding for discriminative inference.
Memisevic R, Sigal L, Fleet DJ., IEEE Trans Pattern Anal Mach Intell 34(4), 2012
PMID: 21808087
Constrained manifold learning for the characterization of pathological deviations from normality.
Duchateau N, De Craene M, Piella G, Frangi AF., Med Image Anal 16(8), 2012
PMID: 22906821
Similarity preserving principal curve: an optimal 1-d feature extractor for data representation.
Sun M, Yang J, Liu C, Yang J., IEEE Trans Neural Netw 21(9), 2010
PMID: 20570770

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