Unsupervised Learning Using Variational Inference on Finite Inverted Dirichlet Mixture Models with Component Splitting

Abstract

Unsupervised learning has been one of the essentials of pattern recognition and data mining. The role of Dirichlet family of mixture models in this field is inevitable. In this article, we propose a finite Inverted Dirichlet mixture model for unsupervised learning using variational inference. In particular, we develop an incremental algorithm with a component splitting approach for local model selection, which makes the clustering algorithm more efficient. We illustrate our model and learning algorithm with synthetic data and some real applications for occupancy estimation in smart homes and topic learning in images and videos. Extensive comparisons with comparable recent approaches have shown the merits of our proposed model.