Semi-supervised Nonnegative Matrix Factorization for Document Classification
This work addresses document classification for researchers and practitioners by offering interpretable models, but it is incremental as it builds on existing nonnegative matrix factorization methods.
The authors tackled document classification by proposing semi-supervised nonnegative matrix factorization models that combine topic modeling and classification, achieving interpretable results on datasets like 20 Newsgroups and Reuters.
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).