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.

Rigon, T. (2023). An enriched mixture model for functional clustering. APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, 39(2), 232-250 [10.1002/asmb.2736].

An enriched mixture model for functional clustering

Rigon, T
2023

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.
Articolo in rivista - Articolo scientifico
Bayesian clustering; Bayesian nonparametrics; functional data analysis;
English
7-dic-2022
2023
39
2
232
250
none
Rigon, T. (2023). An enriched mixture model for functional clustering. APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, 39(2), 232-250 [10.1002/asmb.2736].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/453729
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