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Journal Article

Kernel extrapolation

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons75313

Borgwardt,  KM
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84953

Smola,  AJ
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Vishwanathan, S., Borgwardt, K., Guttman, O., & Smola, A. (2006). Kernel extrapolation. Neurocomputing, 69(7-9), 721-729. doi:10.1016/j.neucom.2005.12.113.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D263-7
Abstract
We present a framework for efficient extrapolation of reduced rank approximations, graph kernels, and locally linear embeddings (LLE) to unseen data. We also present a principled method to combine many of these kernels and then extrapolate them. Central to our method is a theorem for matrix approximation, and an extension of the representer theorem to handle multiple joint regularization constraints. Experiments in protein classification demonstrate the feasibility of our approach.