  We consider the problem of learning a measure of distance among vectors in a
feature space and propose a hybrid method that simultaneously learns from
similarity ratings assigned to pairs of vectors and class labels assigned to
individual vectors. Our method is based on a generative model in which class
labels can provide information that is not encoded in feature vectors but yet
relates to perceived similarity between objects. Experiments with synthetic
data as well as a real medical image retrieval problem demonstrate that
leveraging class labels through use of our method improves retrieval
performance significantly.
