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  Joint Kernel Maps

Weston, J., Schölkopf, B., Bousquet, O., Mann, & Noble, W.(2004). Joint Kernel Maps (131). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D773-A Version Permalink: https://hdl.handle.net/21.11116/0000-0002-8B28-5
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MPIK-TR-131.pdf (Publisher version), 226KB
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Weston, J, Author           
Schölkopf, B1, 2, Author           
Bousquet, O1, 2, Author           
Mann, Author
Noble, WS, 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: We develop a methodology for solving high dimensional dependency estimation problems between pairs of data types, which is viable in the case where the output of interest has very high dimension, e.g. thousands of dimensions. This is
achieved by mapping the objects into continuous or discrete spaces, using joint kernels. Known correlations between input and output can be defined by such kernels, some of which can maintain linearity in the outputs to provide simple (closed form) pre-images. We provide examples of such kernels and empirical results on mass spectrometry prediction and mapping between images.

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 Dates: 2004-11
 Publication Status: Issued
 Pages: 9
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
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 Identifiers: Report Nr.: 131
BibTex Citekey: 3010
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
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Pages: - Volume / Issue: 131 Sequence Number: - Start / End Page: - Identifier: -