mlsanityData to 7 Oct 2026

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

arXiv 2311.07762 ↗First posted 13 Nov 2023stat.ME stat.CO stat.MLcited by 0

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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.

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References · 11

  1. 2013A Mixture of Generalized Hyperbolic Distributionscited by 18
  2. 2015Mixtures of Multivariate Power Exponential Distributionscited by 6
  3. 2018Finite mixtures of matrix-variate Poisson-log normal distributions for three-way count datacited by 6
  4. 2022Breast cancer detection using artificial intelligence techniques: A systematic literature reviewcited by 5
  5. 2014Model-based clustering using copulas with applicationscited by 4
  6. 2017Variational Gaussian Approximation for Poisson Datacited by 4
  7. 2013Mixture of Latent Trait Analyzers for Model-Based Clustering of Categorical Datacited by 3
  8. 2017A Multivariate Poisson-Log Normal Mixture Model for Clustering Transcriptome Sequencing Datacited by 3
  9. 2013Parsimonious Skew Mixture Models for Model-Based Clustering and Classificationcited by 2
  10. 2013A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Settingcited by 1
  11. 2013Variational Bayes Approximations for Clustering via Mixtures of Normal Inverse Gaussian Distributionscited by 1

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