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.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Meinicke, Peter; Klanke, Stefan; Memisevic, R.; Ritter, HelgeUniBi
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
2005
Zeitschriftentitel
IEEE Transactions on Pattern Analysis and Machine Intelligence
Band
27
Ausgabe
9
Seite(n)
1379-1391
ISSN
0162-8828
Page URI
https://pub.uni-bielefeld.de/record/2728085

Zitieren

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, Peter, Klanke, Stefan, Memisevic, R., and Ritter, Helge. 2005. “Principal surfaces from unsupervised kernel regression”. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (9): 1379-1391.
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.

Exploring neighborhoods in the metagenome universe.
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

30 References

Daten bereitgestellt von Europe PubMed Central.

Learning eigenfunctions links spectral embedding and kernel PCA.
Bengio Y, Delalleau O, Le Roux N, Paiement JF, Vincent P, Ouimet M., Neural Comput 16(10), 2004
PMID: 15333211
Unsupervised Learning in a Generalized Regression Framework
meinicke, 2000
Smooth Regression Analysis
watson, Sankhya Series A 26(), 1964

AUTHOR UNKNOWN, 0

hastie, The Elements of Statistical Learning (), 2001

scott, Multivariate Density Estimation (), 1992

AUTHOR UNKNOWN, 0

sch�lkopf, Learning with kernels (), 2002

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Principal Curves and Surfaces
hastie, 1984

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Nonlinear dimensionality reduction by locally linear embedding.
Roweis ST, Saul LK., Science 290(5500), 2000
PMID: 11125150

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

ritter, Neural Computation and Self-Organizing Maps (), 1992
A global geometric framework for nonlinear dimensionality reduction.
Tenenbaum JB, de Silva V, Langford JC., Science 290(5500), 2000
PMID: 11125149

kohonen, Self-Organizing Maps (), 1995

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Kernel Dependency Estimation
weston, Advances in Neural Information Processing Systems 15 (), 2003

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

reiner, Handbook of Global Optimization (), 1995
Learning to Find Pre-Images
bakir, Advances in neural information processing systems (), 2003
Finding the Pre Images in Kernel Principal Component Analysis
kwok, Proc Sixth Ann Workshop Kernel Machines (), 2002
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