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Semi-Supervised Laplacian Regularization of Kernel Canonical Correlation Analysis

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83816

Blaschko,  MB
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84037

Lampert,  CH
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83946

Gretton,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Blaschko, M., Lampert, C., & Gretton, A. (2008). Semi-Supervised Laplacian Regularization of Kernel Canonical Correlation Analysis. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, 133-145.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C7D3-2
Zusammenfassung
Kernel canonical correlation analysis (KCCA) is a dimensionality reduction technique for paired data. By finding directions that maximize correlation, KCCA learns representations that are more closely tied to the underlying semantics of the data rather than noise. However, meaningful directions are not only those that have high correlation to another modality, but also those that capture the manifold structure of the data. We propose a method that is simultaneously able to find highly correlated directions that are also located on high variance directions along the data manifold. This is achieved by the use of semi-supervised Laplacian regularization of KCCA. We show experimentally that Laplacian regularized training improves class separation over KCCA with only Tikhonov regularization, while causing no degradation in the correlation between modalities. We propose a model selection criterion based on the Hilbert-Schmidt norm of the semi-supervised Laplacian regularized cross-covariance operator, which we compute in closed form.