An enriched mixture model for functional clustering |
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Authors: | Tommaso Rigon |
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Institution: | Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy |
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Abstract: | There is an increasingly rich literature about Bayesian nonparametric models for clustering functional observations. Most recent proposals rely on infinite-dimensional characterizations that might lead to overly complex cluster solutions. In addition, while prior knowledge about the functional shapes is typically available, its practical exploitation might be a difficult modeling task. Motivated by an application in e-commerce, we propose a novel enriched Dirichlet mixture model for functional data. Our proposal accommodates the incorporation of functional constraints while bounding the model complexity. We characterize the prior process through a urn scheme to clarify the underlying partition mechanism. These features lead to a very interpretable clustering method compared to available techniques. Moreover, we employ a variational Bayes approximation for tractable posterior inference to overcome computational bottlenecks. |
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Keywords: | Bayesian clustering Bayesian nonparametrics functional data analysis |
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