Kernel Dependency Estimation
Jason Weston, Olivier Chapelle, Andre Elisseeff, Bernhard Schoelkopf and Vladimir Vapnik
We consider the learning problem of
finding a dependency between a general class of objects and another, possibly
different, general class of objects.
The objects can be for example:
vectors, images, strings, trees or graphs. Such a task is made
possible by employing similarity measures in both input and output spaces
using kernel functions and thus embedding the objects into vector spaces.
also make it possible to encode prior information and/or invariances
in the loss function in an elegant way. We experimentally validate our approach on
several tasks: mapping strings to strings, pattern recognition, and reconstruction from partial images.
Example source code (Matlab M-Files)
View image reconstruction results