Distance dependent extensions of the Chinese restaurant process

In this paper we consider the clustering of text documents using the Chinese Restau- rant Process (CRP) and extensions that take time-correlations into account. To this pur- pose, we implement and test the Distance Dependent Chinese Restaurant Process (DD- CRP) for mixture models on both generated and real-world data. We also propose and im- plement a novel clustering algorithm, the Av- eraged Distance Dependent Chinese Restau- rant Process (ADDCRP), to model time- correlations, that is faster per iteration and attains similar performance as the fully dis- tance dependent CRP

Infinite factorization of multiple non-parametric views

Combined analysis of multiple data sources has increasing application interest, in particular for distinguishing shared and source-specific aspects. We extend this rationale of classical canonical correlation analysis into a flexible, generative and non-parametric clustering setting, by introducing a novel non-parametric hierarchical mixture model. The lower level of the model describes each source with a flexible non-parametric mixture, and the top level combines these to describe commonalities of the sources. The lower-level clusters arise from hierarchical Dirichlet Processes, inducing an infinite-dimensional contingency table between the views. The commonalities between the sources are modeled by an infinite block model of the contingency table, interpretable as non-negative factorization of infinite matrices, or as a prior for infinite contingency tables. With Gaussian mixture components plugged in for continuous measurements, the model is applied to two views of genes, mRNA expression and abundance of the produced proteins, to expose groups of genes that are co-regulated in either or both of the views. Cluster analysis of co-expression is a standard simple way of screening for co-regulation, and the two-view analysis extends the approach to distinguishing between pre- and post-translational regulation