Adaptive Variable Clustering in Gaussian Graphical Models
Siqi Sun, Yuancheng Zhu, Jinbo Xu · 2014
Gaussian graphical models (GGMs) are widely-used to describe the relationship be-tween random variables. In many real-world applications, GGMs have a block structure in the sense that the variables can be clustered into groups so that inter-group correlation is much weaker than intra-group correlation. We present a novel nonparametric Bayesian generative model for such a block-structured GGM and an efficient inference algorithm to find the clustering of variables in this GGM by combining a Gibbs sampler and a split-merge Metropolis-Hastings algorithm. Ex-perimental results show that our method per-forms well on both synthetic and real data. In particular, our method outperforms generic clustering algorithms and can automatically identify the true number of clusters. 1