Posterior Regularization on Bayesian Hierarchical Mixture Clustering

Posterior Regularization on Bayesian Hierarchical Mixture Clustering

Bayesian hierarchical mixture clustering (BHMC) improves traditionalBayesian hierarchical clustering by replacing conventional Gaussian-to-Gaussian kernels with a Hierarchical Dirichlet Process Mixture Model(HDPMM) for parent-to-child diffusion in the generative process. However,BHMC may produce tre...

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Veröffentlicht in:arXiv.org 2023-05
Hauptverfasser: Huang, Weipeng, Tin Lok James Ng, Laitonjam, Nishma, Hurley, Neil J
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Bayesian analysis
Clustering
Constraint modelling
Regularization
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creator Huang, Weipeng
Tin Lok James Ng
Laitonjam, Nishma
Hurley, Neil J
description Bayesian hierarchical mixture clustering (BHMC) improves traditionalBayesian hierarchical clustering by replacing conventional Gaussian-to-Gaussian kernels with a Hierarchical Dirichlet Process Mixture Model(HDPMM) for parent-to-child diffusion in the generative process. However,BHMC may produce trees with high nodal variance, indicating weak separation between nodes at higher levels. To address this issue, we employ Posterior Regularization, which imposes max-margin constraints on nodes at every level to enhance cluster separation. We illustrate how to apply PR toBHMC and demonstrate its effectiveness in improving the BHMC model.
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subjects Bayesian analysis
Clustering
Constraint modelling
Regularization
title Posterior Regularization on Bayesian Hierarchical Mixture Clustering
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