Block clustering with collapsed latent block models |
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Authors: | Jason Wyse Nial Friel |
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Institution: | (1) Department of Statistics, University of Glasgow, Glasgow, G12 8QW, U.K |
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Abstract: | We introduce a Bayesian extension of the latent block model for model-based block clustering of data matrices. Our approach
considers a block model where block parameters may be integrated out. The result is a posterior defined over the number of
clusters in rows and columns and cluster memberships. The number of row and column clusters need not be known in advance as
these are sampled along with cluster memberhips using Markov chain Monte Carlo. This differs from existing work on latent
block models, where the number of clusters is assumed known or is chosen using some information criteria. We analyze both
simulated and real data to validate the technique. |
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Keywords: | |
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