Unsupervised feature selection for proportional data clustering via expectation propagation

In this paper, an expectation propagation (EP) inference framework for unsupervised feature selection is proposed for modeling proportional data which naturally appear in many applications such as text and image modeling, in the context of finite mixture-based clustering. Within our framework, simultaneous clustering and feature selection is formalized using finite mixtures of generalizing Dirichlet (GD) distributions. The proposed EP-based inference framework allows to obtain a full posterior distribution on all our unsupervised feature selection model´s parameters. Moreover, the complexity of the deployed mixture models and all the involved model parameters can be evaluated simultaneously. The effectiveness and efficiency of the proposed algorithm are evaluated on both synthetic data and two challenging applications namely human action videos categorization and facial expression recognition.