Generalized Poisson NMF for Overdispersed Count Data
Research Paper#Machine Learning, Matrix Factorization, Count Data, Overdispersion🔬 Research|Analyzed: Jan 3, 2026 08:53•
Published: Dec 31, 2025 03:51
•1 min read
•ArXivAnalysis
This paper addresses the limitations of existing Non-negative Matrix Factorization (NMF) models, specifically those based on Poisson and Negative Binomial distributions, when dealing with overdispersed count data. The authors propose a new NMF model using the Generalized Poisson distribution, which offers greater flexibility in handling overdispersion and improves the applicability of NMF to a wider range of count data scenarios. The core contribution is the introduction of a maximum likelihood approach for parameter estimation within this new framework.
Key Takeaways
- •Addresses the problem of overdispersion in count data for NMF.
- •Proposes a new NMF model using the Generalized Poisson distribution.
- •Introduces a maximum likelihood approach for parameter estimation.
- •Aims to extend the applicability of NMF to a broader class of count data.
Reference / Citation
View Original"The paper proposes a non-negative matrix factorization based on the generalized Poisson distribution, which can flexibly accommodate overdispersion, and introduces a maximum likelihood approach for parameter estimation."