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Hierarchical Bayesian nonparametric mixture models for clustering with variable relevance determination.

Abstract:

We propose a hierarchical Bayesian nonparametric mixture model for clustering when some of the covariates are assumed to be of varying relevance to the clustering problem. This can be thought of as an issue in variable selection for unsupervised learning. We demonstrate that by defining a hierarchical population based nonparametric prior on the cluster locations scaled by the inverse covariance matrices of the likelihood we arrive at a 'sparsity prior' representation which admits a conditiona...

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Publication status:
Published

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Publisher copy:
10.1214/11-ba612

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Institution:
University of Oxford
Department:
Oxford, MSD, Clinical Medicine, WTC Human Genetics
Journal:
Bayesian analysis (Online)
Volume:
6
Issue:
2
Pages:
329-352
Publication date:
2011-07-05
DOI:
EISSN:
1931-6690
ISSN:
1931-6690
URN:
uuid:705dab88-46b6-4ed0-91b1-8a38346a8ade
Source identifiers:
140247
Local pid:
pubs:140247

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