Mixture models | Nicolas Jouvin

Hierarchical clustering with discrete latent variable models and the integrated classification likelihood

Dicrete latent variable models (DLVM) are a unified framework for generative clustering models including mixture models and block models. We propose a new algorithm to greedily maximise the integrated likelihood criterion with respect to a partition in any DLVM where this quantity is analytic.

Greedy clustering of count data through a mixture of multinomial PCA

We propose a new model for the clustering of count data that integrates mixture modeling with the dimension reduction aspect of topic modeling.