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Kernel extrapolation

Vishwanathan, S; Borgwardt, Karsten; Guttman, Omri; Smola, Alexander


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.

CollectionsANU Research Publications
Date published: 2006
Type: Journal article
Source: Neurocomputing
DOI: 10.1016/j.neucom.2005.12.113


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