PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernel Extrapolation
S V N Vishwanathan, Karsten Borgwardt, Omri Guttman and Alex Smola
Neurocomputing 2005.

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2049
Deposited By:S V N Vishwanathan
Deposited On:16 January 2006