# Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count Data

arXiv: https://arxiv.org/abs/2311.07762 · HTML: https://mlsanity.com/paper/2311.07762/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 80 references that are in the map.
- First posted on arXiv 13 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [A Mixture of Generalized Hyperbolic Distributions](https://mlsanity.com/paper/1305.1036/) (2013)
- [Mixtures of Multivariate Power Exponential Distributions](https://mlsanity.com/paper/1506.04137/) (2015)
- [Finite mixtures of matrix-variate Poisson-log normal distributions for three-way count data](https://mlsanity.com/paper/1807.08380/) (2018)
- [Breast cancer detection using artificial intelligence techniques: A systematic literature review](https://mlsanity.com/paper/2203.04308/) (2022)
- [Model-based clustering using copulas with applications](https://mlsanity.com/paper/1404.4077/) (2014)
- [Variational Gaussian Approximation for Poisson Data](https://mlsanity.com/paper/1709.05885/) (2017)
- [Mixture of Latent Trait Analyzers for Model-Based Clustering of Categorical Data](https://mlsanity.com/paper/1301.2167/) (2013)
- [A Multivariate Poisson-Log Normal Mixture Model for Clustering Transcriptome Sequencing Data](https://mlsanity.com/paper/1711.11190/) (2017)
- [Parsimonious Skew Mixture Models for Model-Based Clustering and Classification](https://mlsanity.com/paper/1302.2373/) (2013)
- [A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting](https://mlsanity.com/paper/1306.5368/) (2013)
- [Variational Bayes Approximations for Clustering via Mixtures of Normal Inverse Gaussian Distributions](https://mlsanity.com/paper/1309.1901/) (2013)

## Abstract (arXiv)

A mixture of multivariate Poisson-log normal factor analyzers is introduced by imposing constraints on the covariance matrix, which resulted in flexible models for clustering purposes. In particular, a class of eight parsimonious mixture models based on the mixtures of factor analyzers model are introduced. Variational Gaussian approximation is used for parameter estimation, and information criteria are used for model selection. The proposed models are explored in the context of clustering discrete data arising from RNA sequencing studies. Using real and simulated data, the models are shown to give favourable clustering performance. The GitHub R package for this work is available at https://github.com/anjalisilva/mixMPLNFA and is released under the open-source MIT license.