The supervised hierarchical Dirichlet process

TL;DR

The paper introduces the supervised hierarchical Dirichlet process (sHDP), a nonparametric model that jointly learns clusters from grouped data and associated labels, improving predictive performance in classification and regression tasks.

Contribution

It presents the sHDP model, which effectively incorporates supervision into hierarchical Dirichlet process clustering for grouped data, addressing limitations of previous HDP applications.

Findings

sHDP outperforms sLDA in classification and regression tasks

The model captures group-level structure and label information effectively

Experimental results demonstrate improved predictive accuracy

Abstract

We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group. We compare the sHDP with another leading method for regression on grouped data, the supervised latent Dirichlet allocation (sLDA) model. We evaluate our method on two real-world classification problems and two real-world regression problems. Bayesian nonparametric regression models based on the Dirichlet process, such as the Dirichlet process-generalised linear models (DP-GLM) have previously been explored; these models allow flexibility in modelling nonlinear relationships. However, until now, Hierarchical Dirichlet Process (HDP) mixtures have not seen significant use in supervised problems with grouped data since a straightforward application of the HDP on the grouped data results in learnt clusters that are not predictive of the responses. The sHDP solves this problem by allowing for clusters to be learnt jointly from the group structure and from the label assigned to each group.