Kernel extrapolation
2006
Article
ei
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
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BibTex @article{VishwanathanBGS2006, 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}, doi = {10.1016/j.neucom.2005.12.113}, month_numeric = {3} } |