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Beta-Liouville mixture models have achieved measurable success in many computer vision and pattern recognition applications. In this paper, we develop a novel algorithm to learn this particular kind of models that have been shown to be very efficient for the clustering of proportional data. Our algorithm is based on an accelerated version of the variational Bayes approach. Experiments show that the...
We present a statistical framework for 3D objects modeling and recognition. Our framework is based on describing 3D objects using local descriptors from which a visual vocabulary if built and on a hierarchical Pitman-Yor process mixture of Beta-Liouville distributions. An online approach based on variational Bayes is developed for the learning of the proposed framework. The merits of our model are...
Exploiting Dirichlet process mixture models (also known as infinite mixture models) to model visual and textual data is now standard weapon in the arsenal of machine learning. This paper proposes a new accelerated variational inference approach to learn Dirichlet process mixture models with Dirichlet distributions. The choice of using Dirichlet distribution as the basic distribution is mainly due...
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