Posterior Regularization on Bayesian Hierarchical Mixture Clustering
This is an incremental improvement for clustering tasks in machine learning.
The paper tackles the problem of high nodal variance and weak cluster separation in Bayesian hierarchical mixture clustering by applying posterior regularization with max-margin constraints, resulting in improved model effectiveness.
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.