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  Semi-Supervised Laplacian Regularization of Kernel Canonical Correlation Analysis

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.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C7D3-2 Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C7D4-F
Genre: Konferenzbeitrag

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 Urheber:
Blaschko, MB1, Autor           
Lampert, CH1, 2, Autor           
Gretton, A1, Autor           
Daelemans, Herausgeber
W., Herausgeber
Goethals, B., Herausgeber
Morik, K., Herausgeber
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society, ou_1497647              

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 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.

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 Datum: 2008-08
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
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 Identifikatoren: URI: http://www.springerlink.com/content/k7516n4565312537/fulltext.pdf
DOI: 10.1007/978-3-540-87479-9_27
BibTex Citekey: 5248
 Art des Abschluß: -

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Titel: 19th European Conference on Machine Learning
Veranstaltungsort: Antwerpen, Belgium
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Titel: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Berlin, Germany : Springer
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 133 - 145 Identifikator: -