A nonparametric variable clustering model

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Authors

Konstantina Palla, Zoubin Ghahramani, David Knowles

Abstract

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 clustering, of observed variables so that variables in a cluster are highly correlated. We introduce a Bayesian non-parametric approach to this problem, and demonstrate advantages over heuristic methods proposed to date.