PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Joint Kernel Maps
J. Weston, Bernhard Schölkopf and O. Bousquet
In: Proceedings of the 8th International Work-Conference on Artificial Neural Networks (Computational Intelligence and Bioinspired System), 8-10 Jun 2005, Vilanova i la Geltrú, Barcelona, Spain.

Abstract

We develop a methodology for solving high dimensional dependency estimation problems between pairs of data types, which is viable in the case where the output of interest has very high dimension, e.g., thousands of dimensions. This is achieved by mapping the objects into continuous or discrete spaces, using joint kernels. Known correlations between input and output can be defined by such kernels, some of which can maintain linearity in the outputs to provide simple (closed form) pre-images. We provide examples of such kernels and empirical results.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1020
Deposited By:Bernhard Schölkopf
Deposited On:16 July 2005