The supervised hierarchical Dirichlet process
This work addresses a gap in Bayesian nonparametric models for supervised learning with grouped data, offering a novel approach that could benefit researchers in machine learning and statistics, though it appears incremental as it builds on prior Dirichlet process methods.
The authors tackled the problem of applying Hierarchical Dirichlet Process mixtures to supervised tasks with grouped data, where existing methods fail to learn predictive clusters, and proposed the supervised hierarchical Dirichlet process (sHDP) as a solution, demonstrating its effectiveness on real-world classification and regression problems.
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.