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

Semi-supervised Nonnegative Matrix Factorization for Document Classification