  We describe a nonparametric topic model for labeled data. The model uses a
mixture of random measures (MRM) as a base distribution of the Dirichlet
process (DP) of the HDP framework, so we call it the DP-MRM. To model labeled
data, we define a DP distributed random measure for each label, and the
resulting model generates an unbounded number of topics for each label. We
apply DP-MRM on single-labeled and multi-labeled corpora of documents and
compare the performance on label prediction with MedLDA, LDA-SVM, and
Labeled-LDA. We further enhance the model by incorporating ddCRP and modeling
multi-labeled images for image segmentation and object labeling, comparing the
performance with nCuts and rddCRP.
