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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. In W. Daelemans, B. Goethals, & K. Morik (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008 (pp. 133-145). Berlin, Germany: Springer.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C7D3-2 Version Permalink: https://hdl.handle.net/21.11116/0000-0003-3D62-B
Genre: Conference Paper

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https://link.springer.com/content/pdf/10.1007%2F978-3-540-87479-9_27.pdf (Publisher version)
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 Creators:
Blaschko, MB1, 2, Author           
Lampert, CH1, 2, Author           
Gretton, A1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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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 Dates: 2008-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-540-87479-9_27
BibTex Citekey: 5248
 Degree: -

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Title: 19th European Conference on Machine Learning (ECML PKDD 2008)
Place of Event: Antwerpen, Belgium
Start-/End Date: 2008-09-15 - 2008-09-19

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Title: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008
Source Genre: Proceedings
 Creator(s):
Daelemans, W, Editor
Goethals, B, Editor
Morik, K, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 133 - 145 Identifier: ISBN: 978-3-540-87478-2

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 5211 Sequence Number: - Start / End Page: - Identifier: -