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Kernel extrapolation




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.

Author(s): Vishwanathan, SVN. and Borgwardt, KM. and Guttman, O. and Smola, AJ.
Journal: Neurocomputing
Volume: 69
Number (issue): 7-9
Pages: 721-729
Year: 2006
Month: March
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1016/j.neucom.2005.12.113

Links: Web


  title = {Kernel extrapolation},
  author = {Vishwanathan, SVN. and Borgwardt, KM. and Guttman, O. and Smola, AJ.},
  journal = {Neurocomputing},
  volume = {69},
  number = {7-9},
  pages = {721-729},
  month = mar,
  year = {2006},
  month_numeric = {3}