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Joint Kernel Maps

MPS-Authors
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Weston,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bousquet,  O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Weston, J., Schölkopf, B., & Bousquet, O. (2005). Joint Kernel Maps. In IWANN 2005 (pp. 176-191). Berlin Heidelberg, Germany: Springer-Verlag.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D6DF-F
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.