CUED Publications database

A nonparametric variable clustering model

Palla, K and Knowles, DA and Ghahramani, Z (2012) A nonparametric variable clustering model. Advances in Neural Information Processing Systems, 4. pp. 2987-2995. ISSN 1049-5258

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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 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 blockdiagonal covariance structures in data. We evaluate our method on both synthetic and gene expression analysis problems.

Item Type: Article
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:05
Last Modified: 22 May 2018 06:27