A dimensionally reduced finite mixture model for multilevel data

Recently, different mixture models have been proposed for multilevel data, generally requiring the local independence assumption. In this work, this assumption is relaxed by allowing each mixture component at the lower level of the hierarchical structure to be modeled according to a multivariate Gaussian distribution with a non-diagonal covariance matrix. For high-dimensional problems, this solution can lead to highly parameterized models. In this proposal, the trade-off between model parsimony and flexibility is governed by assuming a latent factor generative model.     

Mixtures of Dirichlet-Multinomial distributions for supervised and unsupervised classification of short text data

Advances in Data Analysis and Classification - Topic detection in short textual data is a challenging task due to its representation as high-dimensional and extremely sparse document-term matrix....