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}
}
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