Dirichlet Process Variable Clustering
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.


This is the core MCMC sampler for the Dirichlet Process Variable Clustering model described in

Konstantina Palla, David A. Knowles and Zoubin Ghahramani. A nonparametric variable clustering model. NIPS 2012.

PDF here: http://papers.nips.cc/paper/4579-a-nonparametric-variable-clustering-model

Factor analysis models effectively summarise the covariance structure of high dimensional data, but the solutions are typically hard to interpret. This motivates attempting to find a disjoint partition, i.e. a simple clustering, of observed variables into highly correlated subsets. We introduce a Bayesian non-parametric approach to this problem, and demonstrate advantages over heuristic methods proposed to date. Our Dirichlet process variable clustering (DPVC) model can discover block-diagonal covariance structures in data. We evaluate our method on both synthetic and gene expression analysis problems.