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A Hierarchical Pitman-Yor mixture of Scaled Dirichlet Distributions

In this paper, we propose a novel clustering method, hierarchical Pitman-Yor process mixture of scaled Dirichlet (SD) distributions, and apply it on a challenging medical application. The flexibility of SD distribution and its good potential to fit non-Gaussian data motivated us to construct our new model on it. Moreover, some challenges in healthcare domain such as high annotation costs of a medical data and sensitivity encouraged us to focus on this unsupervised approach. We learn our proposed model with batch and online variational inference which enable us to estimate model complexity and parameters at the same time. To measure the performance of our model, we evaluated it on human activity recognition and then compared it with other similar alternatives. The results indicate the robustness of our proposed model.