  Transfer learning methods address the situation where little labeled training
data from the "target" problem exists, but much training data from a related
"source" domain is available. However, the overwhelming majority of transfer
learning methods are designed for simple settings where the source and target
predictive functions are almost identical, limiting the applicability of
transfer learning methods to real world data. We propose a novel, weaker,
property of the source domain that can be transferred even when the source and
target predictive functions diverge. Our method assumes the source and target
functions share a Pairwise Similarity property, where if the source function
makes similar predictions on a pair of instances, then so will the target
function. We propose Pairwise Similarity Regularization Transfer, a flexible
graph-based regularization framework which can incorporate this modeling
assumption into standard supervised learning algorithms. We show how users can
encode domain knowledge into our regularizer in the form of spatial continuity,
pairwise "similarity constraints" and how our method can be scaled to large
data sets using the Nystrom approximation. Finally, we present positive and
negative results on real and synthetic data sets and discuss when our Pairwise
Similarity transfer assumption seems to hold in practice.
