Kernel extrapolation

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. Key words: kernel methods, regularization, graph kernels, protein classification.

• Type: Journal
• Year: 2006
• Where: IJON
• Authors: S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri Guttman, Alexander J. Smola
• Keywords: Efficient Extrapolation, IJON 2006, Multiple Joint Regularization, Protein Classification