  Unsupervised models can provide supplementary soft constraints to help
classify new target data under the assumption that similar objects in the
target set are more likely to share the same class label. Such models can also
help detect possible differences between training and target distributions,
which is useful in applications where concept drift may take place. This paper
describes a Bayesian framework that takes as input class labels from existing
classifiers (designed based on labeled data from the source domain), as well as
cluster labels from a cluster ensemble operating solely on the target data to
be classified, and yields a consensus labeling of the target data. This
framework is particularly useful when the statistics of the target data drift
or change from those of the training data. We also show that the proposed
framework is privacy-aware and allows performing distributed learning when
data/models have sharing restrictions. Experiments show that our framework can
yield superior results to those provided by applying classifier ensembles only.
