Minimum-entropy data partitioning using reversible jump Markovchain Monte Carlo |
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Authors: | Roberts SJ Holmes C Denison D |
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Affiliation: | Dept. of Eng. Sci., Oxford Univ.; |
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Abstract: | Problems in data analysis often require the unsupervised partitioning of a data set into classes. Several methods exist for such partitioning but many have the weakness of being formulated via strict parametric models (e.g., each class is modeled by a single Gaussian) or being computationally intensive in high-dimensional data spaces. We reconsider the notion of such cluster analysis in information-theoretic terms and show that an efficient partitioning may be given via a minimization of partition entropy. A reversible-jump sampling is introduced to explore the variable-dimension space of partition models |
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