Statistics and Data Science Seminar
Hsin-Hsiung Huang
University of Central Florida
An Affine-Invariant Bayesian Cluster Process with Split-Merge Gibbs Sampler
Abstract: We develop a clustering algorithm which does not requires knowing the number
of clusters in advance. Furthermore, our clustering method is rotation-, scale- and
translation-invariant. We call it ``Affine-invariant Bayesian (AIB) process".
A highly efficient split-merge Gibbs sampling algorithm is proposed. Using the
Ewens sampling distribution as prior of the partition and the profile residual
likelihoods of the responses under three different covariance matrix structures, we
obtain inferences in the form of a posterior distribution on partitions.
The proposed split-merge MCMC algorithm successfully and efficiently
estimate the
partition. Our experimental results indicate that the AIB process outperforms
other competing methods. In addition, the proposed algorithm is irreducible
and
aperiodic, so that the estimate is guaranteed to converge to the true
partition.
Wednesday April 6, 2016 at 4:00 PM in SEO 636