NIPS Proceedingsβ

A nonparametric variable clustering model

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

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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.