# Semi-supervised Nonnegative Matrix Factorization for Document Classification

arXiv: https://arxiv.org/abs/2203.03551 · HTML: https://mlsanity.com/paper/2203.03551/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 38 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## Builds on (most cited first, 2 of 2)

- [Algorithms for Nonnegative Matrix Factorization with the Kullback-Leibler Divergence](https://mlsanity.com/paper/2010.01935/) (2020)
- [Semi-supervised NMF Models for Topic Modeling in Learning Tasks](https://mlsanity.com/paper/2010.07956/) (2020)

## Built on by (most cited first, 2 of 2)

- [Binary Orthogonal Non-negative Matrix Factorization](https://mlsanity.com/paper/2210.10660/) (2022)
- [Continuous Semi-Supervised Nonnegative Matrix Factorization](https://mlsanity.com/paper/2212.09858/) (2022)

## Abstract (arXiv)

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).