PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernel extrapolation
S V N Vishwanathan, K. M. Borgwardt, Omri Guttman and Alex Smola
Neurocomputing pp. 1-18, 2006.

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. Exper- iments in protein classification demonstrate the feasibility of our approach.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:2030
Deposited By:Alex Smola
Deposited On:16 January 2006