  We propose several new models for semi-supervised nonnegative matrix
factorization (SSNMF) and provide motivation for SSNMF models as maximum
likelihood estimators given specific distributions of uncertainty. We present
multiplicative updates training methods for each new model, and demonstrate the
application of these models to classification, although they are flexible to
other supervised learning tasks. We illustrate the promise of these models and
training methods on both synthetic and real data, and achieve high
classification accuracy on the 20 Newsgroups dataset.
