MoMPCA

<p>The MMPCA package implements the branch &amp; bound classification-Variational
Expectation Maximisation algorithm described in Jouvin et. al.
<a href="https://arxiv.org/abs/1909.00721">https://arxiv.org/abs/1909.00721</a>. It enables the clustering of counts
data such as document/term matrix modeled as a mixture of multinomial PCA
model. Model selection is performed via an approximated form of the
Integrated Classification Likelihood. .</p>

Cluster any count data matrix with a fixed number of variables, such as document/term matrices. It integrates the dimension reduction aspect of topic models in the mixture models framework. Inference is done by means of a greedy Classification Variational Expectation Maximisation (C-VEM) algorithm. An Integrated Classication Likelihood (ICL) model selection is designed for selecting the latent dimension (number of topics) and the number of clusters. For more details, see the article of Jouvin et. al. (2020) <arxiv:1909.00721>.

Nicolas Jouvin

Inference and Clustering for Mixture of Multinomial Principal
Component Analysis

MoMPCA function

<p>The MMPCA package implements the branch &amp; bound classification-Variational
Expectation Maximisation algorithm described in Jouvin et. al.
<a href='https://arxiv.org/abs/1909.00721'>https://arxiv.org/abs/1909.00721</a>. It enables the clustering of counts
data such as document/term matrix modeled as a mixture of multinomial PCA
model. Model selection is performed via an approximated form of the
Integrated Classification Likelihood. .</p>

MoMPCA: MoMPCA: Greedy clustering of count data through a mixture of multinomial PCA

Description

Arguments

Details