Distribution Matching for Transduction
Novi Quadrianto, James Petterson and Alex Smola
In: NIPS 2009, 6-11 Dec 2009, Vancouver, 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.