# Probabilistic semi-nonnegative matrix factorization: a Skellam-based framework

arXiv: https://arxiv.org/abs/2107.03317 · HTML: https://mlsanity.com/paper/2107.03317/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications](https://mlsanity.com/paper/1803.01257/) (2018)
- [Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm](https://mlsanity.com/paper/1611.05010/) (2016)
- [Exact and Heuristic Algorithms for Semi-Nonnegative Matrix Factorization](https://mlsanity.com/paper/1410.7220/) (2014)
- [Non-negative Matrix Factorization: Robust Extraction of Extended Structures](https://mlsanity.com/paper/1712.10317/) (2017)
- [Tight Semi-Nonnegative Matrix Factorization](https://mlsanity.com/paper/1709.04395/) (2017)

## Abstract (arXiv)

We present a new probabilistic model to address semi-nonnegative matrix factorization (SNMF), called Skellam-SNMF. It is a hierarchical generative model consisting of prior components, Skellam-distributed hidden variables and observed data. Two inference algorithms are derived: Expectation-Maximization (EM) algorithm for maximum a posteriori estimation and Variational Bayes EM (VBEM) for full Bayesian inference, including the estimation of parameters prior distribution. From this Skellam-based model, we also introduce a new divergence D between a real-valued target data x and two nonnegative parameters λ0 and λ1 such that Dleft(xmidλ0,λ1right)=0Leftrightarrow x=λ0-λ1, which is a generalization of the Kullback-Leibler (KL) divergence. Finally, we conduct experimental studies on those new algorithms in order to understand their behavior and prove that they can outperform the classic SNMF approach on real data in a task of automatic clustering.