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2012 NeurIPS NeurIPS 2012

A nonparametric variable clustering model

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.

🐣 Hot Topic Early Bird - high-dimensional data

Authors

Konstantina Palla, Zoubin Ghahramani, David A. Knowles

Topics

Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling Machine Learning > Core Methods > Clustering Machine Learning > Learning Types > Unsupervised Learning Machine Learning > Optimization & Theory > Bayesian Inference Machine Learning > Bayesian & Probabilistic > Bayesian Learning Machine Learning > Core Methods > Dimensionality Reduction Machine Learning > Bayesian & Probabilistic > Nonparametric Bayesian

Keywords

unsupervised learning factor analysis bayesian nonparametrics covariance structure variable clustering disjoint partition probabilistic model high-dimensional data

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