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).
@article{arxiv.2203.03551,
title = {Semi-supervised Nonnegative Matrix Factorization for Document Classification},
author = {Jamie Haddock and Lara Kassab and Sixian Li and Alona Kryshchenko and Rachel Grotheer and Elena Sizikova and Chuntian Wang and Thomas Merkh and RWMA Madushani and Miju Ahn and Deanna Needell and Kathryn Leonard},
journal= {arXiv preprint arXiv:2203.03551},
year = {2022}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2010.07956