S V N Vishwanathan, Karsten Borgwardt, Omri Guttman and Alex Smola
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.