# Continuous Semi-Supervised Nonnegative Matrix Factorization

arXiv: https://arxiv.org/abs/2212.09858 · HTML: https://mlsanity.com/paper/2212.09858/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [A critical review of LASSO and its derivatives for variable selection under dependence among covariates](https://mlsanity.com/paper/2012.11470/) (2020)
- [Semi-supervised NMF Models for Topic Modeling in Learning Tasks](https://mlsanity.com/paper/2010.07956/) (2020)
- [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)

## Abstract (arXiv)

Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. The technique amounts to an approximation of a nonnegative matrix as the product of two nonnegative matrices of lower rank. In this paper, we show this factorization can be combined with regression on a continuous response variable. In practice, the method performs better than regression done after topics are identified and retrains interpretability.