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Model selection in kernel methods based on a spectral analysis of label information

: Braun, M.L.; Lange, T.; Buhmann, J.M.


Franke, K.; Müller, K.R.; Nickolay, B.; Schäfer, R. ; Deutsche Arbeitsgemeinschaft für Mustererkennung -DAGM-:
Pattern recognition : 28th DAGM Symposium. Proceedings : Berlin, Germany, September 12-14, 2006
Berlin: Springer, 2006 (Lecture Notes in Computer Science 4174)
ISBN: 3-540-44412-2
ISBN: 978-3-540-44412-1
Deutsche Arbeitsgemeinschaft für Mustererkennung (Symposium) <28, 2006, Berlin>
Fraunhofer FIRST ()

We propose a novel method for addressing the model selection problem in the context of kernel methods. In contrast to existing methods which rely on hold-out testing or try to compensate for the optimism of the generalization error, our method is based on a structural analysis of the label information using the eigenstructure of the kernel matrix. In this setting, the label vector can be transformed into a representation in which the smooth information is easily discernible from the noise. This permits to estimate a cut-off dimension such that the leading coefficients in that representation contains the learnable information, discarding the noise. Based on this cut-off dimension, the regularization parameter is estimated for kernel ridge regression.