# Semi-supervised NMF Models for Topic Modeling in Learning Tasks

arXiv: https://arxiv.org/abs/2010.07956 · HTML: https://mlsanity.com/paper/2010.07956/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Guided Semi-Supervised Non-negative Matrix Factorization on Legal Documents](https://mlsanity.com/paper/2201.13324/) (2022)
- [Semi-supervised Nonnegative Matrix Factorization for Document Classification](https://mlsanity.com/paper/2203.03551/) (2022)
- [Supervised Dictionary Learning with Auxiliary Covariates](https://mlsanity.com/paper/2206.06774/) (2022)
- [Stratified-NMF for Heterogeneous Data](https://mlsanity.com/paper/2311.10789/) (2023)
- [Continuous Semi-Supervised Nonnegative Matrix Factorization](https://mlsanity.com/paper/2212.09858/) (2022)
- [Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topics](https://mlsanity.com/paper/2505.16493/) (2025)

## Abstract (arXiv)

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.