  We propose new semi-supervised nonnegative matrix factorization (SSNMF)
models for document classification and provide motivation for these models as
maximum likelihood estimators. The proposed SSNMF models simultaneously provide
both a topic model and a model for classification, thereby offering highly
interpretable classification results. We derive training methods using
multiplicative updates for each new model, and demonstrate the application of
these models to single-label and multi-label document classification, although
the models are flexible to other supervised learning tasks such as regression.
We illustrate the promise of these models and training methods on document
classification datasets (e.g., 20 Newsgroups, Reuters).
