Distribution matching for transduction
Novi Quadrianto, James Petterson and Alex Smola
In: 23rd Annual Conference on Neural Information Processing Systems, 7-12 Dec 2009, Vancouver, B.C., Canada.
Many transductive inference algorithms assume that distributions
over training and test estimates should be related, e.g. by
providing a large margin of separation on both sets. We use this
idea to design a transduction algorithm which can be used without
modification for classification, regression, and structured
estimation. At its heart we exploit the fact that for a good
learner the distributions over the outputs on training and test
sets should match. This is a classical two-sample problem which can
be solved efficiently in its most general form by using
distance measures in Hilbert Space. It turns out that a number of
existing heuristics can be viewed as special cases of our approach.