# On a Guided Nonnegative Matrix Factorization

arXiv: https://arxiv.org/abs/2010.11365 · HTML: https://mlsanity.com/paper/2010.11365/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Guided Semi-Supervised Non-negative Matrix Factorization on Legal Documents](https://mlsanity.com/paper/2201.13324/) (2022)
- [Stratified-NMF for Heterogeneous Data](https://mlsanity.com/paper/2311.10789/) (2023)
- [Nonnegative Matrix Factorization to understand Spatio-Temporal Traffic Pattern Variations during COVID-19: A Case Study](https://mlsanity.com/paper/2111.03592/) (2021)
- [Learning nonnegative matrix factorizations from compressed data](https://mlsanity.com/paper/2409.04994/) (2024)
- [Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topics](https://mlsanity.com/paper/2505.16493/) (2025)

## Abstract (arXiv)

Fully unsupervised topic models have found fantastic success in document clustering and classification. However, these models often suffer from the tendency to learn less-than-meaningful or even redundant topics when the data is biased towards a set of features. For this reason, we propose an approach based upon the nonnegative matrix factorization (NMF) model, deemed textitGuided NMF, that incorporates user-designed seed word supervision. Our experimental results demonstrate the promise of this model and illustrate that it is competitive with other methods of this ilk with only very little supervision information.